text:
\section*{Acknowledgements}
We thank Wen Xiao for providing the output of PRIMERA.
We would also like to thank anonymous reviewers for valuable comments and suggestions.
Research was supported in part by US DARPA KAIROS Program No. FA8750-19-2-1004 and INCAS Program No. HR001121C0165, National Science Foundation IIS-19-56151, IIS-17-41317, and IIS 17-04532, and the Molecule Maker Lab Institute: An AI Research Institutes program supported by NSF under Award No. 2019897, and the Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) by NSF under Award No. 2118329. Any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and do not necessarily represent the views, either expressed or implied, of DARPA or the U.S. Government. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
\section{Method}
Here we provide more details about our method part. The workflow of \textsc{GranuSum} and case study are listed in Table~\ref{tab:overall_case}.

\input{Table/event_example}

\input{Table/overall_case}

\subsection{Event Extraction}
\label{sec:appendix_ee}
Specifically, given a sentence $s$, we use a dependency parser to obtain its dependency parse tree and select all non-auxiliary verbs as centric tokens.
Then, along the syntactic relationships between the selected verbs and other tokens, we extract the longest phrase that matches the designed patterns as events.
As illustrated in Table~\ref{tab:pattern}, the most frequent pattern is $n_1$-nsubj-$v_1$, such as \textit{Hurricane hit}. Another common pattern is $n_1$-nsubj-$v_1$-dobj-$n_2$, like \textit{Hurricane damage buildings}.
Here ``nsubj'' denotes an active relationship between nouns and verbs, while ``nsubjpass'' in another example represents a passive relationship between them. More detailed examples can be found in Table~\ref{tab:overall_case}, we extract events from four selected sentences, and the colored text shows the locations of the events in the original document.

\subsection{Event-based Summarizer Pre-training}
\label{sec:appendix_summarizer}
We further explain the four steps of Event-based Summarizer pre-training with the help of the following example.
For a paragraph of news as \textit{``Honduras braced for potential catastrophe Tuesday. Hurricane Mitch roared through the Caribbean, churning up high waves and intense rain that sent coastal residents scurrying for safer ground. President declared a state of maximum alert and the Honduran military sent planes to pluck residents from their homes on islands near the coast''}, we

1) first randomly select a sentence: \textit{``Hurricane Mitch roared through the Caribbean, churning up high waves and intense rain that sent coastal residents scurrying for safer ground''},

2) extract events in it such as \textit{Mitch roar}, \textit{Mitch churn up wave and rain}, \textit{send} and \textit{resident scurry},

3) then mask this sentence in the original paragraph, and finally

4) use extracted events and masked text as the input and regard the selected sentence as the target as follows:

\begin{itemize}
    \item Input: Mitch roar | Mitch churn up wave and rain | send | resident scurry $\langle$seg$\rangle$ Honduras braced for potential catastrophe Tuesday. $\langle$mask$\rangle$ President declared a state of maximum alert and the Honduran military sent planes to pluck residents from their homes on islands near the coast.
    \item Target: Hurricane Mitch roared through the Caribbean, churning up high waves and intense rain that sent coastal residents scurrying for safer ground.
\end{itemize}

In our experiments, we randomly mask 1 to $n$ sentences from a document, which leads to $n$ samples to pre-train our Summarizer.
Here we set $n$ to the smaller of a constant number 10 and one-third of the number of sentences in the document.

\subsection{Event Selector}
\label{sec:appendix_selector}
We use the example in Table ~\ref{tab:overall_case} to further explain the flow of the Event Selector.
When we obtain candidate events from selected sentences, there are still different types of issues in the candidate set. Some generic and uninformative events, such as ``\textit{club say}'' and ``\textit{let him know}'', should have a lower priority for a summary. Although we introduce sentence-level redundancy score in the pruning step, as a finer-grained unit, events still suffer from redundancy problem (see events in Table~\ref{tab:overall_case} with the same color), e.g., both ``win MVP'', ``Malone win MVP'' and ``average 31.1 points and 14.7 rebounds'', ``average 24.5 points and 15.3 rebounds'' appear in the candidate set. However, after the events ranking and filter using our Event Selector, all of these issues are alleviated. In this case, our Selector regards ``Malone win MVP'', ``Moses Malone die'' and ``Malone be remember'' as the three most salient events, which is consistent with the original news. In addition, uninformative events (``\textit{club say}'' and ``\textit{let him know}'') are ranked at the end of the candidate sets, and duplicate events (``win MVP'' and ``average 24.5 points and 15.3 rebounds'') are filtered out due to the lowest salience score. In general, the reasonable ranking of candidate events by
the Selector plays a crucial role in improving the quality of subsequent multi-granularity summaries.

\subsection{Multi-Granularity Summary Generation}
\label{sec:appendix_generation}
We can see from Table~\ref{tab:overall_case}, to obtain the most condensed summary, the two most important events (``Malone win MVP'' and ``Moses Malone die'') and the original news are fed to the model. Then, the pre-trained Summarizer can be aware of event-based cues and generate the corresponding sentence: ``Moses Malone, a three-time NBA MVP and one of basketball's most ferocious rebounders, died on Sunday''. As more events are input, our Summarizer also has the ability to adjust the order of the narrative to make the content more logical. In the summary of granularity level 2, the order in the prompt is ``Malone be remember'' then ``team compile a 65-17 record'', but the model first output "He helped the team compile a 65-17 record in the first season" and then "These achievements make him be remembered as a genuine icon and pillar in the history of 76ers basketball" to make the whole summary more coherent and intuitive.
Compared to sentences selected from the source documents (see Step 1 in Table~\ref{tab:overall_case}), the summary generated by GranuSum omits unimportant details and paraphrases to make it more concise.
Abstractive models without guidance signals, such as PEGASUS, tend to generate some repetitive sentences (the first two sentences), and generate several less relevant sentences without capturing important events.
In contrast, \textsc{GranuSum} can output summaries that are more relevant and faithful to the original text.

\section{Examples for GranuDUC}

We provide two annotation examples for our proposed GranuDUC benchmark in Table~\ref{tab:annotation}.

\input{Table/annotation}
\section{Conclusion}

In this paper, we highlight the importance of multi-granularity summarization systems in catering to user preferences and applying them to real-world scenarios.
To facilitate research in this direction, we propose the first unsupervised multi-granularity summarization framework \textsc{GranuSum} and build a well-established testbed.
Experiments demonstrate the effectiveness of our framework.
\section{Experiments}
We design three settings of experiments:

\noindent 1) experiments on GranuDUC,

\noindent 2) bucket-based evaluation,

\noindent 3) unsupervised abstractive summarization.

\noindent The first two settings constitute a new testbed for multi-granularity summarization, where bucket means that we divide the existing dataset into different buckets according to semantic coverage to make the evaluation more comprehensive.
In addition to this scenario, the last experiment auxiliarily evaluates the quality of summaries generated by our framework under the conventional unsupervised abstractive summarization setting.

\subsection{Experimental Setup}

\paragraph{Datasets}
Because the conclusions obtained on the summarization dataset of a single domain are not generalizable~\cite{wang2019exploring, zhong2019closer,chen2020cdevalsumm}, we select two widely varying domains: news and scientific papers for our experiments
Notably, we focus on two types of datasets, multi-document and long-document summarization, which are two main scenarios where users call for a multi-granularity system.
For multi-document summarization, we concatenate the multiple articles into a single sequence as the source text.
In addition to our benchmark GranuDUC, we use the following three datasets. Detailed statistics are listed in Table~\ref{tab:statistics}.

\underline{Multi-News}~\cite{fabbri2019multi} is a large-scale multi-document summarization dataset in the news domain. We use it in bucket-based evaluation (Section~\ref{bucket_sec}) and unsupervised summarization experiments (Section~\ref{unsupervsied_sec}).

\underline{DUC2004}~\cite{dang2005overview} contains 50 clusters, each with 10 relevant news articles and 4 reference summaries written by humans. Due to its small size, it is usually used directly as a test set. We utilize it in the unsupervised summarization experiment (Section~\ref{unsupervsied_sec}).

\input{Table/statistics}

\underline{arXiv}~\cite{cohan2018discourse} is a collection of long documents derived from scientific papers. It takes the full text of the paper as input, and the corresponding abstract as the reference summary. We use it in the unsupervised summarization experiment (Section~\ref{unsupervsied_sec}).

\paragraph{Implementation Details} To process long input text in Table~\ref{tab:statistics}, we choose the  Longformer-Encoder-Decoder~(LED)~\cite{beltagy2020longformer} as our backbone model, and train it with typical cross entropy loss.
For Multi-News and arXiv, we further pre-train LED with our event-related generation task on their training corpora  (without using reference summaries) for a total of 10,000 and 30,000 steps, respectively.
We set batch size to 32 and the maximum learning rate to 2e-5. $\lambda_{1}$ in the importance score is 1.0 and $\lambda_{2}$ is 0.4.
By tuning the hyperparameters on the validation set, we empirically extract 9 sentences for Multi-News and 4 sentences for arXiv to form a candidate set, and input 90\% events according to salience score to the Summarizer under unsupervised summarization setting.
For DUC2004 and GranuDUC, we test directly with the Summarizer pre-trained on Multi-News, since these datasets are both in the news domain. In all experiments, we use standard pyrouge\footnote{\url{pypi.python.org/pypi/pyrouge/0.1.3}} to calculate ROUGE scores. Due to the limitation of computational resources, we truncate an input text to 3,072 tokens for LED models.

\paragraph{Baselines}
We use the following baselines:

\underline{BART}~\cite{lewis2020bart} is the state-of-the-art sequence-to-sequence pre-trained model for various generation tasks, including abstractive dialogue generation, question answering, and text summarization. We use BART-large in all the experiments.

\underline{PEGASUS}~\cite{zhang2020pegasus} is a powerful generation model with gap-sentences generation as a pretraining objective tailored for abstractive summarization. We use the large version of PEGASUS for comparison.

\underline{PEGASUS-event} indicates that on top of PEGASUS, additional event information is prepended to the input before the $\langle$mask$\rangle$ token.
We compare it to see if additional event information can be captured without our event-aware pre-training stage.

\underline{LED}~\cite{beltagy2020longformer} has the same architecture as BART, except that the attention in the encoder introduces additional local attention and extends the position embedding to 16K tokens by copying the original embedding. The parameters in the LED are initialized by the weights in BART.

\underline{LED-Length-Control} (LED-LC) is a baseline that we obtained by further pre-training LED. Inspired by \citet{fan2018controllable}, given a document and the desired number of sentences $k$, we randomly place $k$ sentences in the document with the $\langle$mask$\rangle$ token, and let the model recover these sentences. During inference, we input the text and the desired number of sentences as a hint to the model so that it can control the length of the output summary.\footnote{If we need a two-sentence summary, the input format is: ``$\langle$2$\rangle$ $\langle$seg$\rangle$ $\langle$mask$\rangle$ source documents''. It is exactly the same as \textsc{GranuSum} in terms of the training details and data.}

\underline{PRIMERA}~\cite{xiao2022primera} is a pre-trained model for multi-document summarization that reduces the need for dataset-specific architectures and extensive labeled data. It achieves state-of-the-art results on multi-document summarization datasets under multiple settings.

\subsection{Multi-granularity Evaluation}

The first testbed we built for multi-granularity summarization includes two evaluation methods:

\noindent 1) To test the ability of the model to generate summaries with different granularity levels when given the same input, we evaluate different models on our benchmark GranuDUC.

\noindent 2) To supplement the limited size of GranuDUC, we design a bucket-based evaluation approach, where we divide a large-scale test set into different buckets based on their granularity levels, and test the ability of models to generate quality summaries in different granularity buckets.

\input{Table/granuduc}

\input{Table/bucket}

\subsubsection{Results on GranuDUC}
The summaries of each sample in GranuDUC can be divided into three granularity levels, where coarse granularity level represents the most compact summary, and fine granularity level is the most fine-grained summary. We use automatic metrics ROUGE and perform the human evaluation to evaluate the performance of different models in GranuDUC. Notably, both LED-LC and \textsc{GranuSum} have the ability to adjust the output according to specific granularity scenarios. At three different granularity levels on GranuDUC, we let LED-LC output 1, 3 and 8 sentences which correspond to the average length of reference summaries at different granularities. For our model, we take the top 90\% events with the highest salience score in the selected 1, 3, 8 sentences as the input hint.
For all baselines, we control the length of the model output to be similar to the reference summary to get the best performance.

\input{Table/unsupervised}

\paragraph{Automatic Evaluation} As illustrated in Table~\ref{tab:granuduc}, compared to PEGASUS, LED-LC can bring a certain degree of improvement due to the ability to control the length of the output summary. This improvement is not remarkable at fine granularity level. For coarse and medium granularity levels, LED-LC can control the number of output sentences, while PEGASUS does not have a similar capability and it can only generate shorter summaries by truncating the output (to 32 and 64 words), which leads to performance degradation. On the other hand, \textsc{GranuSum} exceeds LED-LC and PEGASUS by a large margin in all the granularity levels. Although \textsc{GranuSum} and LED-LC are trained on the same data, \textsc{GranuSum} increases the R-1 score by 1.78 at coarse granularity level (21.83$\rightarrow$23.61), and the improvement reaches to 4.53 at fine granularity level (30.18$\rightarrow$34.71). With the benefit of event information, our model can generate more relevant and quality summaries, and the advantage is more pronounced in fine-grained summaries. Therefore, \textsc{GranuDUC} is a more suitable system for multi-granularity scenarios than existing controllable summarization models.

\paragraph{Human Evaluation} We also conduct human evaluation to have a more comprehensive understanding of the model output. Six graduate students are involved in this process to score the generated summaries from three different perspectives: fluency, relevance and faithfulness to the source documents. The score range is 1-5, with 1 being the worst and 5 the best. Each sample requires two people to discuss and agree on the scoring. According to the fluency scores in Table~\ref{tab:granuduc}, both LED-LC and \textsc{GranuDUC} can generate coherent sentences, while PEGASUS performs poorly in coarse and medium granularity levels due to truncating the output to a fixed length. From the perspective of relevance and faithfulness, a clear trend is that the more fine-grained the summary, the more relevant it is to the original text and the more likely it is to contain factual errors. Specific to the models, \textsc{GranuSum} generates more relevant and faithful summaries in all granularity scenarios compared to other baselines by exploiting event information.

\subsubsection{Bucket-based Evaluation}
\label{bucket_sec}
In addition to GranuDUC, we seek to utilize existing large-scale datasets for multi-granularity evaluation.
Unlike the previous approach of using a single reference summary to evaluate multiple lengths of summaries~\cite{shapira2018evaluating}, we divide the reference summaries into different buckets based on semantic coverage and then compare the performance of each model in each bucket.
We first design a metric to calculate the granularity score between the source document and the reference summary to categorize the different samples. Because the same events in original text and human-written summary may have different descriptions, we design a granularity score on the basis of BERTScore~\cite{zhang2019bertscore} to perform soft matching due to its ability to measure semantic coverage between two sequences. Specifically, we extract all the events in the source document and the reference summary as two event sequences, and calculate \textbf{\underline{Granularity Score}} as:

\vspace{-2mm}
\begin{align}
\mathrm{Granu}(D, r) = f(Event_D, Event_r),
\end{align}

\noindent where $D$ is the source documents and $r$ represents the reference summary.
$Event_D$ denotes that we extract all events from $D$ by using the approach in Section~\ref{sec:ee}, and  concatenate them into an event sequence.
$f$ means that BERTScore is used to calculate the recall score between two event sequences.
Intuitively, a high recall score of the reference summary to the original text indicates that it has high semantic coverage and thus it is a summary at a high granularity level.
We sort all samples in the test set of Multi-News dataset according to Granularity Score and divide them into three buckets with the same number of samples. The average length of summaries in the three buckets are 198, 214, and 236 words, respectively.

Although PRIMERA is the state-of-the-art model, it does not have the flexibility to change the output in response to different buckets. For LED-LC, we let the model generate 7, 8, and 9 sentences in low, medium, and high buckets, respectively. For our model, we take the top 70\%, 80\%, and 90\% of the events with the higher salience score (see Section~\ref{selector}) in 9 selected sentences as the input for three different buckets. As shown in Table~\ref{tab:bucket}, LED-LC has no significant benefits over PRIMERA, indicating that controlling the output length and ignoring its connection to the original text is not a good solution for the multi-granularity system. In contrast, \textsc{GranuSum} achieves substantial improvements in all buckets compared to powerful baselines. In particular, in buckets with high semantic coverage, our model improves R-1 score by 3.28 compared to PRIMERA. Also, ``- Ranking'' means that we no longer filter out events based on the salience score, which causes a performance drop. It confirms that our selector can indeed exclude irrelevant and redundant events and thus improve the quality of the generated summary.

\subsection{Unsupervised Abstractive Evaluation}
\label{unsupervsied_sec}
The quality of the summary is a key factor for all summarization systems. So in addition to the multi-granularity scenario, we likewise compare \textsc{GranuSum} with conventional unsupervised abstractive summarization models. Table~\ref{tab:unsupervised} provides results on three datasets. The first section includes a simple yet effective approach LEAD, which refers to extracting the first few sentences at the beginning of the text as a summary. It is a strong baseline in the news domain due to the lead bias problem~\cite{see2017get,zhong2019searching}.
The second section lists the strong baselines and the last section contains the results of our models. Selector indicates that we extract several sentences from the source document based on our importance score described in Section~\ref{selector} as the summary.

Surprisingly, although \textsc{GranuSum} is not specially designed for the conventional unsupervised summarization task, it still beats all the competitors and achieves new state-of-the-art results on most metrics across datasets.
Despite inputting the same hints, PEGASUS-event does not show the ability to exploit event information and even performs worse than PEGASUS.
In contrast, our pre-trained Event-aware Summarizer incorporates event information well into the generated summaries and thus boosts performance.
Furthermore,  \textsc{GranuSum} outperforms Selector, which is a strong extractive baseline, and extractive approaches usually dominate unsupervised summarization tasks.
We think the improvement comes from two reasons:

\noindent 1) In the pre-training stage, important content in the masked sentences is easier to reconstruct due to the redundancy of input texts. Thus, \textsc{GranuSum} learn to filter those unimportant content in inference, generating more concise summaries.

\noindent 2) Event Selector screens out less critical events which should not appear in the summary.

\noindent Overall, \textsc{GranuSum} improves R-1 score by 1.0 on average compared to the previous best results, indicating that it is sufficient to generate quality summaries besides the multi-granularity ability.

\section{Introduction}
Text summarization aims to condense and summarize long documents into a concise paragraph containing the essential points of the original texts~\cite{see2017get,liu2019text,wang2020heterogeneous,DBLP:conf/acl/ZhongLCWQH20,liu2022brio,an2022colo}.
Notably, the requirements for summarization are highly customized and personalized for different users~\cite{diaz2007user,lerman2009sentiment,yan2011summarize,chen2021dialogsum}. Therefore, generating quality summaries to meet different preferences should be a natural capability of summarization systems.

\input{Table/intro_case}

Granularity, a key aspect of customization in summarization, is used to measure the degree of semantic coverage between summary and source documents~\cite{mulkar2011granularity}.
To cater to the diverse needs of readers, the granularity level of summaries often varies in a wide range.
As shown in Table~\ref{tab:intro_case}, given multiple news about Hurricane Mitch, the most compact summary (Coarse Granularity Level) accommodates only the most important event to help people grasp the overall picture of the input documents.
Interested readers, on the other hand, may prefer more fine-grained summaries (Medium and Fine Granularity Level) to acquire additional details, such as how many casualties were caused and how different countries aided Honduras.
Thus, multi-granularity summaries can meet the intent of different users and are more versatile in real-world applications.

Most existing summarization models and benchmarks focus solely on single-granularity summarization.
It limits the ability of these systems to adapt to different user preferences and generalize to a wider range of granularity scenarios.
To alleviate this issue, some recent studies are dedicated to controlling the length of summary~\cite{kikuchi2016controlling, fan2018controllable, liu2018controlling}.
However, as a surface-level feature of the summary, longer length does not equate to a higher degree of semantic coverage.
In other words, the length limit can be easily satisfied by talking less/more details about the same event, but this is in contrast with the concept of summarization.
Another research direction is query/aspect-based ~\cite{zhong2021qmsum, hayashi2021wikiasp, ge2021fine} and interactive summarization~\cite{shapira2017interactive, shapira2021extending}. Based on different queries, models can focus on different parts of the document and create summaries of various granularities. In practice, it requires a user to provide a query, implying that the user must have prior knowledge of the topic of the source text.
Therefore, automatic granularity-aware summarization model is still an under-explored topic.

In this paper, we propose an unsupervised multi-granularity summarization framework called \textsc{GranuSum}.
Unlike previous work based on supervised learning to provide guidance signals, such as salient sentences~\cite{dou2021gsum}, keywords~\cite{he2020ctrlsum}, and retrieved summaries~\cite{an2021retrievalsum}, our approach does not rely on any manually labeled data.
To measure the granularity, we first regard events as the basic semantic units of the input texts because events carry rich semantic information and are considered as informative representations in many NLP tasks~\cite{zhang2020aser,li2020connecting,chen2021event}.
Overall, our system consists of two event-related components: Event-aware Summarizer and Event Selector. Specifically, given the document and randomly selected events in it as hints, we pre-train an abstractive Summarizer that can recover event-related passages.
Furthermore, in an unsupervised manner, our Event Selector selects the events with high salience from the original text by candidate events pruning and ranking.
Finally, through selecting different numbers of anchor events based on Event Selector, we can control the Summarizer to generate summaries containing different events, thus covering different numbers of semantic units of the original text.
With our proposed approach, \textsc{GranuSum} becomes an unsupervised framework for multi-granularity summary generation.

To evaluate the multi-granularity summarization systems,
we re-annotate DUC2004~\cite{dang2005overview} as the first benchmark in this direction (denoted as \textit{GranuDUC}).
Given multiple documents on the same topic, we annotate summaries at three levels of granularity with different semantic coverage.
Also, to utilize the existing datasets for a supplement evaluation,  we propose to divide several large-scale summarization datasets into buckets with summaries at different granularity levels to further evaluate the model performance.
Experimentally, \textsc{GranuSum} surpasses strong summarization systems on all the multi-granularity evaluations. Additionally, we conduct conventional unsupervised abstractive summarization experiments on three typical benchmarks in different domains. Results demonstrate that \textsc{GranuSum} also substantially improves the previous state-of-the-art model under the traditional setting.

\section*{Limitations}

We state the limitations of this paper from the following four aspects:

1) Unlike previous work that uses summary length to approximate granularity, we adopt an event-based definition, which can be extended to be more flexible.
For example, introducing phrases, entities, relationships, etc. as part of the granularity may be a feasible way to further enhance the granularity-aware summarization system.

2) Despite being the first multi-granularity summarization benchmark, GranuDUC can only be used as a test set due to its small size.
Thus, we call for the emergence of customized summarization datasets, which can greatly facilitate the development of customizable summarization models.

3) Specific to the method, we extract events from the source text as hints, which may reduce the abstractness of the generated summaries to some extent.
In pursuit of a more abstractive summary, rephrasing events into different forms may be a viable option, and we leave it as future work.

4) In this paper we focus on three different levels of granularity and take document clusters containing thousands of words as input.
A promising extension could be to input longer text and to add finer levels of granularity, for example, to generate summaries for an entire book (e.g., a novel) at multiple granularities.

\section{Multi-Granularity Framework}

\begin{figure}[t]
    \centering
    \includegraphics[width=1\linewidth]{./Figure/GranuSum_model.png}
    \caption{Overview of \textsc{GranuSum}. It consists of two components: Event Selector and Event-aware Summarizer. The red line ($\rightarrow$) indicates that Selector extracts the salient events from the text, and the dotted line means that Summarizer assists in this process. The blue line ($\Rightarrow$) denotes the multi-granularity summary generation process. By inputting different numbers of events as anchors (purple and green boxes), \textsc{GranuSum} can generate multi-granularity summaries.}
    \label{fig:model}
\end{figure}

In this section, we first describe in detail our framework \textsc{GranuSum}, which has two major components: Event-aware Summarizer and Event Selector. Combining them enables multi-granularity generation. The overall framework can be seen in Figure~\ref{fig:model}. Then, we introduce the new human-annotated benchmark, GranuDUC, which can be used for multi-granularity evaluation.

\subsection{Event-Aware Summarizer}
\label{sec:ee}
In this work, we focus on abstractive summarization approaches.
The way we make the model perceive the granularity is by inputting hints with different degrees of specificity, and here we format the hints as a sequence of events.

\paragraph{Event Extraction} We follow previous work to define an event as a verb-centric phrase~\cite{zhang2020aser}.
A lightweight method\footnote{Code for this part is available at: \url{https://github.com/yzjiao/Open-vocabulary-event-extraction}.} is utilized to extract events from open-domain unstructured data: we extract frequently-occurring syntactic patterns that contain verbs as events.
On the basis of \citet{zhang2020aser}, we extend a total of 76 syntactic patterns for matching events.
For instance, the most common patterns contain $n_1$-nsubj-$v_1$ (e.g., \textit{Hurricane hits}) and $n_1$-nsubj-$v_1$-dobj-$n_2$ (e.g., \textit{Earthquake damages buildings}).\footnote{nsubj and dobj indicate nominal subject and direct object. They are different relations between verbs and nouns.}
More details and concrete examples can be found in Appendix~\ref{sec:appendix_ee}.

\paragraph{Event-based Summarizer Pre-training}
Previous studies reveal that event information can be an effective building block for models to perform text generation~\cite{daniel2003sub,glavavs2014event}, so we attempt to obtain a Summarizer with the ability to generate event-related text in an unsupervised way.
In the pre-training phase, it is trained to regenerate sentences based on a list of events and the remaining source text.
Then we use it to generate a summary at inference time.
Concretely, we pre-train a sequence-to-sequence model in the following steps:

\noindent 1) randomly select a few sentences from the text,

\noindent 2) extract events in these selected sentences,

\noindent 3) mask these sentences in the source document,

\noindent 4) take extracted events and unmasked text as input.

\noindent Then we use these selected sentences as the target for the model.
For example, for a dialogue text as \textit{``Do you have any plans tomorrow? How about playing basketball? Sure, I just finished my homework, it's time to exercise.''}, we can select \textit{How about playing basketball?} and extract the event \textit{play basketball}.
In this case, the specific format given to the model is:

\begin{itemize}
    \item Input: play basketball $\langle$seg$\rangle$ Do you have any plans tomorrow? $\langle$mask$\rangle$ Sure, I just finished my homework, it's time to exercise.
    \item Target: How about playing basketball?
\end{itemize}

\noindent where $\langle$seg$\rangle$ is the segmentation token and $\langle$mask$\rangle$ indicates that a sentence at this position is masked.
We use `|` token to split the different events, and another example in news domain to further explain the four steps can be found in Appendix~\ref{sec:appendix_summarizer}.

\subsection{Event Selector}
\label{selector}
The salience of the selected events determines whether the Summarizer can generate a quality summary or an irrelevant and uninformative paragraph.
A long document can contain hundreds of events, and finding the best event subset involves an exponential search space.
Therefore, it is crucial to have an Event Selector that selects the most important events in the text to feed to the Summarizer. Our event selector first reduces the search space by pruning out less salient events and sentences, and then ranks the remaining events using the pre-trained Summarizer.

\paragraph{Event Ranking} The salience of the different events extracted from the documents varies.
Some of the events are informative and relevant to the original text, but others are too general or specific.
For instance, two events \textit{club say} and \textit{Malone be remember} can be extracted from the sentence \textit{``The club said Malone will forever be remembered as a genuine icon and pillar in the Philadelphia 76ers team"}.
The former is not important for this news, while the latter is indispensable.
And in a sentence \textit{``Malone won MVP awards by averaging 24.5 points and 15.3 rebounds"}, \textit{``average 24.5 points and 15.3 rebounds''} is too detailed to be included in a high-level summary.
Thus, ranking candidate events is a key function of Event Selector.

Inspired by \citet{yuan2021bartscore}, where a pre-trained generative model is capable of evaluating the correlation between the input and the target, we also use our pre-trained Event-based Summarizer to calculate the salience score for each event.
Given the candidate event set $E$ and the source document $D$, our Summarizer can generate a candidate summary $c_{E}$.
Whenever an event $e$ in the input is removed, if the generated candidate summary $c_{E\setminus\{e\}}$ differs greatly from $c_{E}$, this indicates that the removed event $e$ is salient.
As in the example above, removing \textit{``club say"} does not cause an obstacle for the model to recover the sentence whose main meaning is that Malone is remembered by people, while removing \textit{``Malone be remember"} makes the model unable to output the correct sentence.
Thus, the latter should be the more important event.
Formally, the \textbf{\underline{Salience Score}} of event $e$ can be defined as:
\begin{align}
    \label{equ:salience}
    \mathrm{Sal}(e) &\stackrel{\text{def}}{=} -\mathrm{Sim}(c_{E\setminus\{e\}};c_{E}), \\
    \mathrm{Sim}(x_1, x_2) &\stackrel{\text{def}}{=}\mathrm{R1(x_1,x_2)+R2(x_1,x_2)},
\end{align}
where $\mathrm{Sim}(x_1, x_2)$ is a function based on ROUGE score~\cite{lin2004rouge} to measure the similarity between any two text sequences $x_1$ and $x_2$. $\mathrm{R1}$ and $\mathrm{R2}$ are ROUGE-1 and ROUGE-2 scores, respectively.
Based on the salience score, Event Selector can rank all the events in the candidate set. However, a single sentence may contain multiple events, so a long document can encompass hundreds of events. Using all events as a candidate set leads to unaffordable computational consumption. Therefore, we prune the candidate events before ranking them.

\paragraph{Candidate Pruning} We expect to capture a small set of events that are relevant to the main topic while pruning redundant parts.
Events with high relevance provide an efficient summary of the central points in the original text, while low redundancy ensures that the final summary is concise.
To this end, we first select several salient sentences and extract the events in them as a candidate set.
For relevance, if a sentence has a high semantic overlap with other input sentences, it should have a higher centrality and a higher probability to be included in the summary~\cite{padmakumar2021unsupervised}. Thus, we define the \textbf{\underline{Relevance Score}} of each sentence as:
\begin{align}
    \mathrm{Rel}(s, D) \stackrel{\text{def}}{=} \mathrm{Sim}(s;D \setminus \{s\}),
\end{align}

\noindent where $s$ means the sentence and $D$ represents the given document. $D\setminus\{s\}$ indicates that the sentence $s$ is removed from the original text $D$.

For redundancy, the sentences in the summary should contain low redundant information when compared with each other.
So when extracting the $k$-th sentence, we define its \textbf{\underline{Redundancy Score}} as follows:
\begin{align}
\mathrm{Red}(s, S) \stackrel{\text{def}}{=} \sum_{i=1}^{k-1} \mathrm{Sim}(s_{i};s),
\end{align}

\noindent where $S$ is a set of the $k$-1 sentences in the summary so far.
We follow the idea of Maximal Marginal Relevance~\cite{DBLP:conf/sigir/CarbonellG98} to maximize relevance and minimize redundancy to calculate the \textbf{\underline{Importance Score}} of each sentence as:
\begin{align}
\mathrm{Imp(s, S, D)} =
\lambda_{1} \mathrm{Rel}(s, D) - \lambda_{2} \mathrm{Red}(s, S).
\end{align}

Through iteratively calculating the score of each sentence, we can eventually obtain a fixed number of sentences and extract the events from them as a candidate set.

\subsection{Multi-Granularity Summary Generation}
With Event-aware Summarizer and Event Selector, it is feasible to generate multi-granularity summaries.
By taking different numbers of ranked events as hints, the Summarizer can perceive the specific level of semantic coverage required to enable the generation of different summaries.
For example, the Summarizer can generate a concise coarse-grained summary when only the two events with the highest salience scores (see Equation \ref{equ:salience}) are input.
A case study to illustrate the overall flow of the multi-granularity summary generation can be found in Appendix~\ref{sec:appendix_generation}.
During inference, instead of using the same setting as \citet{DBLP:conf/icml/ZhangZSL20}, i.e., placing the $\langle$mask$\rangle$ token at the beginning of the article, we simply omit it.
Because we already provide enough event information to guide the model to generate a summary in our framework.

\subsection{New Benchmark: GranuDUC}
Considering that there is no dataset for evaluating multi-granularity summarization models, we re-annotate a new benchmark called GranuDUC on the basis of DUC2004~\cite{dang2005overview}. Our annotation team consists of 5 graduate students in NLP or people with equivalent expertise. For each document cluster, annotators are required to read multiple source documents and write summaries at three different granularities.
The annotators are informed to be aware that granularity is not distinguished by the number of sentences, but is defined by different semantic coverage of the original text.
Specifically, we inform the annotators that ``coarse granularity level" should include only the main event of the entire documents, ``medium granularity level" should include several important conditions, results and processes surrounding the main topic, and "fine granularity level" should further include the details such as time and location for each sub-event.
Summaries at different granularities require significantly different levels of semantic coverage.
Newly annotated sentences are allowed to be copied or rewritten from DUC2004's original reference summaries. In addition, we require annotators not to use the same sentences in different summaries of a sample, even when describing the same event. Each annotated summary is required to be reviewed by another annotator, then these two people discuss and revise until an agreement is reached. In the end, GranuDUC contains a total of 50 clusters, each cluster contains an average of 10 related documents and 3 summaries of different granularity, ranging from 10 words to more than 200 words in length. To demonstrate the quality of GranuDUC, we include the annotations of two samples in Appendix~\ref{tab:annotation}.
\section{Related Work}

\subsection{Customized Summarization}

In order to meet the needs of different users, existing neural summarization systems attempt to control customization of the summary, such as the aspects of content~\cite{zhong2021qmsum,hayashi2021wikiasp}, summary length~\cite{christensen2014hierarchical,kikuchi2016controlling,liu2018controlling} and writing style~\cite{an2021retrievalsum}. Also, several studies seek to accommodate multiple types of preferences simultaneously to achieve customized summarization. \citet{fan2018controllable} additionally introduces different special marker tokens to the model to generate user-controllable summaries. \citet{he2020ctrlsum} allows for entity-centric, length-controllable, and question-guided summarization by adjusting the prompts, i.e., changing the textual input in the form of a set of keywords or descriptive prompt words.
However, the unavailability of large-scale data containing customized summaries limits the development of these systems that rely on supervised learning.
Thus, we focus on unsupervised approaches and are committed to solving the granularity aspect, which remains an under-explored direction in customized summarization.

\subsection{Unsupervised Summarization}

In contrast to supervised learning, unsupervised models do not require any human-annotated summaries during training. Unsupervised summarization can also be divided into two branches: extractive methods and abstractive approaches. Most extractive methods rank the sentences and select the highest-ranked ones to form the summary. Specifically, they score sentences based on graph~\cite{erkan2004lexrank,hirao2013single,parveen2015topical}, centrality~\cite{zheng2019sentence,liang2021improving}, point-wise mutual information~\cite{padmakumar2021unsupervised}, or sentence-level self-attention in pre-trained models~\cite{xu2020unsupervised}. Another direction is unsupervised abstractive approaches, and these studies typically employ sequence-to-sequence auto-encoding method~\cite{chu2019meansum} with adversarial training and reinforcement learning~\cite{wang2018learning}. In addition, \citet{yang2020ted} pre-train a Transformer model for unsupervised abstractive summarization by exploiting the lead bias phenomenon~\cite{see2017get,zhong2019searching} in the news domain. In this work, our framework is an unsupervised abstractive framework, and can be further enhanced on top of the extractive method.

redpajama_set_name: RedPajamaArXiv


text:
Margaret Hamilton
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(born 1936). American computer scientist Margaret Hamilton was one of the first computer software programmers; she created the term software engineer to describe her work. She helped to write the computer code for the command and lunar modules used on the Apollo missions to the Moon in the late 1960s and early '70s (see space exploration).
Hamilton was born Margaret Heafield on August 17, 1936, in Paoli, Indiana. She received a bachelor's degree in mathematics from Earlham College in Richmond, Indiana, in 1958. She subsequently married James Hamilton and taught high school mathematics for a short time. The couple moved to Boston, Massachusetts, where Margaret planned on attending Brandeis University to study abstract mathematics. In the meantime, however, she accepted a job at the Massachusetts Institute of Technology (MIT), where she began programming software to predict the weather. She also did postgraduate work there in meteorology.
At MIT's Lincoln Laboratory Hamilton worked on the Semi-Automatic Ground Environment (SAGE) project, America's first air defense system. She wrote software for a program to identify enemy aircraft. Hamilton next worked at MIT's Instrumentation Laboratory (now the independent Charles Stark Draper Laboratory), which provided aeronautical technology for the National Aeronautics and Space Administration (NASA). She led a team that was given the task of developing the software for the guidance and control systems of the in-flight command and lunar modules of the Apollo missions. Since there were no schools that taught software engineering, the team members had to work out any problems on their own. Hamilton herself specifically concentrated on software to detect system errors and to recover information in a computer crash. Both those elements were crucial during the Apollo 11 mission, which successfully took astronauts Neil Armstrong and Edwin ("Buzz") Aldrin, Jr., to the Moon.
Hamilton left MIT in the mid-1970s to work in the private sector. She cofounded the company Higher Order Software in 1976 and founded Hamilton Technologies 10 years later.
NASA awarded Hamilton the Exceptional Space Act Award in 2003 to honor her contribution to the success of the Apollo missions. President Barack Obama presented her with the U.S. Presidential Medal of Freedom in 2016.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Toronto's Massey Hall is undergoing renovation, and part of the process is to restore something many people weren't aware that Massey Hall even had: a number of stained glass windows. Eve Guinan, owner of EGD Glass, is overseeing a team of five glaziers that are part of a larger project to bring the old glass back to its former glory.
Hidden for nearly a century, Massey Hall's stained glass windows find new life
By David FriendThe Canadian Press
Tue., April 9, 2019timer4 min. read
When Massey Hall opened its doors 125 years ago visitors were awestruck by the choral music venue's spectacular stained glass windows, says restoration expert Eve Guinan.
In the daytime, sunlight poured through the magenta and rose panels, casting a transcendent glow over the audience, while at night the lemon and blue hues glimmered in the moonlight. Portraits of 12 famous composers adorned the Toronto building's exterior, from Bach and Beethoven to Handel and Chopin.
Despite Massey being a strictly secular venue, it must have been a downright spiritual experience — but it wouldn't last long. Those windows were boarded up in the early half of the 20th century to muffle noise leaking in from the streets.
Over the years, as Massey evolved into a space that hosted famous names from around the world, the windows fell into a state of disrepair.
"From the outside, the stained glass was so dirty nobody actually knew it existed," explains Guinan.
"It was, as we call it, encrusted with grime."
Guinan's team of five glaziers at Toronto company EGD Glass are sharing the job of fixing the windows with another restoration firm, Vitreous Glass. They scrub away the dirt, replace cracked glass and touch up blemishes on the composers' faces in an effort to rescue the 93 historic windows.
It's part of Massey's two-year "revitalization" project, valued at $139 million and set to be completed in late 2020, which will inject new life into the venue.
Massey was made legendary by its performers — including Glenn Gould, Gordon Lightfoot and Neil Young, who famously recorded his Live at Massey Hall 1971 album in the space — but its stained glass windows carry their own storied history.
They debuted when Massey Hall opened in the summer of 1894, created by the Faircloth Brothers, a Toronto shop that operated directly across the street from the venue.
When the owners of the venue boarded up a number of the windows to isolate the building from persistent outside noise, their decorative accent was all but forgotten.
Guinan learned of their neglect when she was hired in 1996 to patch up some of the attic windows. She found many of them had been spray painted with ceiling paint in the 1960s, she said. It led her to become involved in a more significant effort to save the windows — an initiative slowed by the usual financial setbacks and red tape.
When the necessary heritage approvals were granted to remove the windows in 2017, Guinan and the owners of Massey discovered just how badly neglect had worn on them.
"Pigeons had got between the glass and the plywood and it was completely full of straw and all sorts of material for nesting," she said.
"There were (bird) skeletons, old beer bottles. It was quite a treasure trove of stuff there."
They also learned windows for two of the composers — Bach and Beethoven — were missing from the collection. Decades earlier, both were removed to make room for the fire exits. Many people assumed they were lost forever, perhaps stolen by a sneaky collector.
The publicized hunt for the missing faces eventually led to the basement of Roy Thomson Hall, where they were found in unmarked boxes.
Guinan doesn't spend much time dwelling on that story, since each painted composer is now meticulously catalogued inside her warehouse awaiting his restoration.
Before that happens, each window is dismantled into its individual pieces, carefully washed and reassembled like a jigsaw puzzle.
Since they're considered heritage artifacts, the guidelines are strict around what can be done with each face.
The original stained glass painting can't be modified, so instead the touch-ups are made on another layer of glass that's placed overtop of the original work, not all that different than modifications on a picture in Photoshop.
When they're installed, each restored window will be placed behind a layer of clear glass protecting it from outside elements. On the inside, a blackout blind will close off the windows during concerts while another thick piece of soundproof glass will prevent those persistent noise problems.
"It's like a big, huge sandwich," Guinan says.
The stained glass windows project will be finished around midsummer, she adds, because the construction of the building relies on it. Interior details like the woodwork can't be started until the windows are installed.
Guinan is hopeful the windows will add the perfect touch to bridge the past and present at Massey Hall.
"It just proves, I think, that heritage can be part of modern as well," she said.
"We don't have to knock it down and build something new."
The interconnected stories of Toronto's stained-glass artists
Where are Bach and Beethoven? Tracking down two stained-glass windows missing from Massey Hall
'It was electric' — Musicians share their top Massey Hall memories before it closes


Libra is one of the most loving and sensitive zodiac signs and since it is ruled by the planet Venus, you can rarely see them single. They love to love and be loved and they believe in eternal love. When they fall in love, they fall hard and there isn't a thing they wouldn't do for their significant one. Libras are known to be sweet, sensitive and the one who always goes the extra mile in love relationships. But if you were wondering how to win the heart of that kind of perfect man, worry no more.
Here is a list of the things every Libra man loves the most and if you practice them, you will be the one who will make a Libra man fall in love with you. In the blink of an eye, he will be head over heels in love with you and you can bet that he will do anything to make you feel good and loved. Trust me, a man like this a true keeper!
It is a known fact that Libra men are attracted to confidence, so if you don't have it, you should work on that. They like to have a partner who feels good in their own skin and who doesn't compete with others to look good. They like girls who are down to earth and any kind of extremism will just push them away. So, just be confident enough and know your worth. Every Libra will recognize that and he will stick to you.
When you meet a Libra, you will see that they like spontaneity and that they enjoy crazy things. So, when you are with them, you can relax a little bit more and do things that you didn't think you will ever try. They simply like to see that their partner feels good in their own skin and that they are happy with their life.
All the astrologist agree that Libra men like to be complimented so that can be your strongest weapon. Compliment him about the things he does because you will boost his ego in that way and he will feel as if he pleases all your needs.
Clothes are quite important in a relationship with a Libra man because he hates short skirts and tops but likes women who dress elegant. Taste is something that has a high price to him and he cherishes women who can dress elegantly and still be seductive.
When you see that a Libra man is interested in you, start a new tactic of seduction. Tell him that you have your own things going on and that you will meet him after. Don't answer immediately when he calls or texts but let him chase you. If he sees you are easy to get, he will lose interest immediately.
The worst thing that you can do is to be clingy around a Libra man. He loves his freedom the most and if you act all needy, he will lose interest in you. Show him that you are good on your own and that you are having fun even if he is not there. That will show him that you are independent and he will like you even more.
Libra is known as someone who changes his mind 100 times in a day and in the end, you really don't know what he wants. Be patient because he doesn't know how to act differently. He is trying the best he can to have a normal relationship with you but sometimes he just needs more time to make up his mind. And if you give it to him, he will cherish you more than you think.
You don't need to tell him all about yourself. Keep some things for you only and make sure that he makes some effort to get to know you better. If you tell him all about you at first, you won't be interesting to him and you will just push him away. Be smart and leave some things for yourself only.
When you meet a Libra man, you won't be able to make him open up to you emotionally right away. That's why you should be patient and try to win him over in a different way. Invite him for dinner, light some candles and walk with him in the moonlight. That kind of traditional romance will definitely make him fall for you.
Don't just stick to the familiar things, but make sure that you try something you thought you would never do. Show him that you ready for some challenges and action and that you are not afraid of dealing with new things in your life. He will admire you and it will make him fall for you. Who wouldn't?
Every Libra man likes small surprises. It can be dinner prepared just for him or tickets to his favorite movie. These unexpected happenings will mean a lot to him. If you do this, he will think that you cherish him and that you are wife material. I am sure he will be happy to have someone like you around and he will go the extra mile to keep you as a part of his life.
If you want to make a Libra man fall in love with you, you should be honest and kind. If he sees that you help others who have problems, it will make you perfect in his eyes and he will fall head over heels in love with you. He simply loves good people and it will mean a lot to him to have an honest and good partner like you.
It is a fact that you can't push any man to do things he is not ready for and you won't be able to that with Libra as well. So, if you see that he is not ready for some things, for example commitment, give him time to make up his mind. If you pressure him to do something as soon as possible, there is a chance he will just walk away.
I am sure your Libra does his very best to make you feel good, so you can also try to be kind and thankful to him. Don't forget to tell him that you cherish all his attention and the things he does for you. He will be glad to hear that and you will feel good. And if you continue with that kind of behavior, you might succeed in making a long-term relationship.


The form wizard in a great tool in Access 2016 that will enable you to create forms and sub forms quickly and easily (So long as your tables are set up correctly). The creation of forms makes the difference between entering data into database much like an spreadsheet, and entering information like an actual datbase.
Using the form wizard in Access 2016 you will create a form along with a sub form. During this tutorial you will see how to use the steps in the wizard to create forms. In the next stages you will see how to create a button to link to other forms and pass information across from one form to another.


Consider me your newest follower, dropping by from TSL!
I just am not getting how the top of the knit ends up being even across when it goes down further in the middle of the shorts? Does that make sense? In the final picture, the knit is straight across the top, not dipping in the middle front at all. I've seen similar tutorials on other websites too, but I can't get my brain around it! I'd like to understand before I start a project, can you help me?
Hi There! That is a good question! It's been a while since I wore my maternity shorts now but from memory they do dip down a little at the lowest point but because the fabric is so stretchy it kinda molds to the shape of your belly nicely and the dip is really not a problem.
The photos do look like it goes straight across but that is possibly because you are seeing the back of the knit band (which is straight across) and the way the knit band is sitting. Sorry this was one of my first blog posts and doesn't have the best photos (I've learnt a whole lot since then!).


HM270 is primarily used for structural or conventional type glazing and curtainwall sealing involving glass, plastic or metal. Also good for sealing skylights, solar panels, ventilators, aluminum siding and windows, heating/air conditioning units, duct work, recreational vehicles and other glass, plastic or metal assemblies.
Not recommended for the following applications: Use with concrete, marble, brass, copper, oily woods, limestone, brick, lead surfaces, PTFE, polyethylene, polypropylene and related materials; joints subject to continuous water immersion (swimming pools); horizontal joints or traffic bearing joints where abrasion resistance is required (walkways, driveways, runways); totally confined or air-free spaces; sealing green or vulcanized rubber; use in potable water systems; direct contact on the reflective coatings on mirrors; applications requiring painting as paint will not adhere to silicone.


Are you looking majer shirts	and majer hoodies?. There are many T-Shirts, Longsleeve Tee, Sweatshirts, Hoodies, V-neck, Sweaters, Meaning, Tank Top about your name Majer here. Check it now!
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Years ago, I attended an introductory workshop on HTML and CSS hosted by Ladies Learning Code. I gained more than knowledge of coding that day; I realized that I could be a creator, not a mere consumer, of technology. This empowering experience drove me to spend the summer of 2014 learning front-end web development, which eventually established my career in web design and development.
My experiences working in the fields of technology and information have been incredibly eye-opening. I am fortunate to be exposed to a wide range of information and technology phenomena. Now, I am starting my journey in the discipline of information studies to examine and investigate these phenomena in depth and to contribute directly to shaping the relationships between information, technology, and people.
© 2019 Copyright Keli Chiu. Powered by	WordPress & HTML5 Blank.


text:
Q: How can I pass XHTML Form data into a Java bean class variable? I'm trying to pass form text from my XHTML file into my java bean class into a variable called userNumber but cannot work out how to pass the user inputted number into the java bean class variable.
Here is the code for the XHTML file:

    <h:head>
        <title>Numbers Page</title>
    </h:head>
    <h:body>

Your guess: <h:form> <input type="text" name="numberGuess">

        <h:commandButton value="Play" action="game_result"/>
        </input></h:form>

    </h:body>

Here is the code for the java bean class:
import java.util.Random;
import javax.faces.bean.ManagedBean;
import javax.faces.bean.RequestScoped;

@ManagedBean
@RequestScoped
public class GameBean
{
    public String getNumber()
    {
        String userNumber = request.getparameter("numberGuess");
        return userNumber;
    }

    public int getLuckyNumber()
    {
        Random number = new Random();
        return number.nextInt(1000000)+1;
    }
}

Please help! Thanks!

A: Adnan I think you are learning jsf and have a misunderstanding of how jsf works and the model binding if offers compared against servlets and jsp that is more focused in request and response http objects abstraction, in fact in jsf you could get values from the request but is not correct way to do.
For you case you could bind the value in this way

    <h:head>
        <title>Numbers Page</title>
    </h:head>
    <h:body>

Your guess:
           <h:form>
                <h:inputText value="#{game.number}"/>
                <h:commandButton value="Play" action="game_result"/>
           </h:form>

    </h:body>

And your managed bean must be
@ManagedBean(name="game")
@RequestScoped
public class GameBean
{
   private String number;
   public String getNumber() {
      return this.number;
   }
   public void setNumber(String number) {
      this.number = number;
   }
}

Note that I set the name of the bean as game @ManagedBean(name="game") and it's bind to the input <h:inputText value="#{game.number}"/> in the property number of the bean game, in this way jsf set the value in the property name the value of the input each time it send the form to the server.
You could also get the request value in this way
FacesContext.getCurrentInstance().getExternalContext().getRequestHeaderValuesMap().get("numberGuess");

redpajama_set_name: RedPajamaStackExchange


Netflix's Chief Content Officer, Ted Sarandos offered to Reuter's reporters, '"Being in a position to precisely forecast and predict the behavior of that many people on fairly radical change is one thing we'll get better at all enough time. Now, it's possible to sit at his place searching and ordering any medicine then there is completely no necessity of searching it in multiple stores. It is common for banks and customers to help remedy any business with caution that has an element of 'high-risk' in it.
The last a couple of years usually demand a lot of clinical work. Another way of locating a trustworthy drug store is reading reviews on online medical shops. People may consider utilising homeopathic treatments just since they're quite low-cost.
Shop and compare the values offered when compared to the local pharmacy. While generally it's always best to stick with one, you may find yourself having accounts at multiple stores depending on your own different needs. In most states physician assistants can prescribe medication also.
This online pharmacy is absolutely very great for old ailing patients and physically disabled persons. Screaming, yelling, demanding things, or just being rude won't help the situation. com or call 877 636 Rx - Rx - About The Founders - Sandy A Reese Rigg, RN, Rph - Susan Chin Jackson, RPh - Both graduated from St.
This concerns almost everything starting from your ordering of treatment each of the way to delivery. So if you happen to be looking to move forward and embrace this new digital age and create your own E Commerce pharmacy I suggest you please take a look at. On the opposite hand, the cost with the medicine of online pharmacy will likely be low because there is absolutely no such ad of them that the other has.


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Recent review: Jenna & Ash, Thank you for making my little boys birthday perfect. From booking to the finer details everything was hassle free. Thank you.


The Fabulous Forum orchestra section with SkyDeck visible above.
As part of the $100-million reinvention, 71 tons of steel were installed overhead, significantly increasing the structural capacity of the roof. With the ability to support 350,000 pounds of hanging weight, the Forum is now equipped to accommodate the largest touring concerts and productions.
SkyDeck™'s safe walkable surface adds additional benefit to the venue. The woven wire work surface covers 34 thousand square feet, allowing for any rigger to reach any access point safely and easily.
Set up at The Forum for the benefit event for the Lustgarten Foundation with SkyDeck in visible above.
Congratulations! Here's to another fabulous fifty years!
Learn more about the storied venue. It's a good read.


text:
Neighboring States
What Are the Official Positions of Egypt on Israel and the Palestinian Territories?
What Are the Official Positions of Iran on Israel and the Palestinian Territories?
What Are the Official Positions of Iraq on Israel and the Palestinian Territories?
What Are the Official Positions of Jordan on Israel and the Palestinian Territories?
What Are the Official Positions of Lebanon on Israel and the Palestinian Territories?
What Are the Official Positions of Saudi Arabia on Israel and the Palestinian Territories?
What Are the Official Positions of Syria on Israel and the Palestinian Territories?
What Are the Official Positions of Turkey on Israel and the Palestinian Territories?


text:
Anna Vennerston1
Anna Vennerston is the daughter of Axel Vennerston.1 She married, firstly, Gosta Otto Rudolf Axelson von Schoultz.1 She married, secondly, Per Hugo Huitfeldt.1 She married, thirdly, Lt.-Col. Arthur James Herbert Grey, son of Colonel Leopold John Herbert Grey and Josephine Catherine Van Cortlandt, on 22 December 1925.1 She married, fourthly, Cyril Denzil Branch on 16 September 1963.1 She married, fifthly, Clarence V. Frayn in January 1978.1
Her married name became Huitfeldt.1 Her married name became von Schoultz.1 From 22 December 1925, her married name became Grey.1 From 16 September 1963, her married name became Branch.1 From January 1978, her married name became Frayn.1 She lived in 2003 at 8403 142 Street, Edmonton, Alberta, CanadaG.1
Child of Anna Vennerston and Lt.-Col. Arthur James Herbert Grey
Reverend Axel Robert Henry Grey+1 b. 30 Jan 1933
Axel Vennerston1
Axel Vennerston was Grand Marshal of the Court of Sweden.1 He lived at Stockholm, SwedenG.1
Child of Axel Vennerston
Anna Vennerston+1
Gosta Otto Rudolf Axelson von Schoultz1
Gosta Otto Rudolf Axelson von Schoultz married Anna Vennerston, daughter of Axel Vennerston.1
Per Hugo Huitfeldt1
Per Hugo Huitfeldt married Anna Vennerston, daughter of Axel Vennerston.1
Cyril Denzil Branch1
Cyril Denzil Branch married Anna Vennerston, daughter of Axel Vennerston, on 16 September 1963.1 He died in 1976.1
Clarence V. Frayn1
Clarence V. Frayn married Anna Vennerston, daughter of Axel Vennerston, in January 1978.1
He lived at Edmonton, Alberta, CanadaG.1
Reverend Axel Robert Henry Grey1
Reverend Axel Robert Henry Grey was born on 30 January 1933.1 He is the son of Lt.-Col. Arthur James Herbert Grey and Anna Vennerston.1 He married Zenith Catherine Gallagher, daughter of James Gallagher, on 14 December 1954.1
He was educated at Wellington College, Wellington, Berkshire, EnglandG.1 He was educated at St. Andrew's College, Grahamstown, Cape Province, South AfricaG.1 He graduated from St. Pauls Theological College, Grahamstown, Cape Province, South AfricaG, in 1958 with a L.Th.1
Child of Reverend Axel Robert Henry Grey and Zenith Catherine Gallagher
Paul Grey1 b. 28 Apr 1958
Simon James Holliday Gray1
M, #303098, b. 21 October 1936, d. 6 August 2008
Last Edited=2 Sep 2008
Simon James Holliday Gray was born on 21 October 1936 at Hayling Island, Hampshire, EnglandG.1 He married, firstly, Beryl Kevern in 1965.1,2 He married, secondly, Hon. Victoria Katherine Rothschild, daughter of Nathaniel Mayer Victor Rothschild, 3rd Baron Rothschild and Teresa Georgina Mayor, in 1997.1,2 He died on 6 August 2008 at age 71 at London, EnglandG.1
He was educated at Dalhousie University, Halifax, Nova Scotia, CanadaG.1 He was educated at Cambridge University, Cambridge, Cambridgeshire, EnglandG.1 He wrote the book Wise Child, published 1967.1 He wrote the book Butley, published 1971.1 He wrote the book Otherwise Engaged, published 1977.1 He was a lecturer at Queen Mary College, London University, London, EnglandG.1 He wrote the book Quartermaine's Terms, published 1981.1 He wrote the book The Common Pursuit, published 1984.1 He wrote the book The Smoking Diaries, published 2004.1 He wrote the book The Year of the Jouncer, published 2006.1 He wrote the book The Last Cigarette, published 2008.1
Children of Simon James Holliday Gray and Beryl Kevern
Benjamin Gray1
Lucy Gray1
[S219] Announcements, New York Times, New York, U.S.A., 8 August 2008. Hereinafter cited as New York Times.
Beryl Kevern1
Beryl Kevern married Simon James Holliday Gray in 1965.1,2 She and an unknown person were divorced in 1997.2
From 1965, her married name became Gray.1
Children of Beryl Kevern and Simon James Holliday Gray
Benjamin Gray is the son of Simon James Holliday Gray and Beryl Kevern.1


YEEEAAAH, TANK GIRL!!!! Lost among the myriad of failed adaptations in the 1990s lies a fantastic Looney-Toonish feminist manifesto that clearly scared too many people in Hollywood. Listen to Alex and Julio as they discuss Lori Petty's powerhouse performance as a female Bugs Bunny superhero, Malcolm McDowell's prescient take on a future American president and Naomi Watts' heartbreaking film debut in The States. All this and Ice-T as a mutant kangaroo too!
Not enough for you? We'll also announce our Summer 2018 Project!


If you were hoping for a chance to stay in an open-air luxury suite on the Great Wall of China, don't book your plane ticket quite yet. Airbnb has cancelled their stay on the world heritage site.
The home stay company's Night At program has invited guests to stay in unexpected locations like Dracula's Castle in Transylvania, a Canadian Taco Bell, and the top of an Olympic ski jump outside Oslo. However after announcing their plans for a Night At the Great Wall where contest winners would win a one night stay in a customized guard tower on the Wall, a multi-course gourmet dinner, and traditional Chinese entertainment, plans have been shelved.
In a statement on their site, Airbnb wrote that while "there was an agreement in place" that lead them to announce the contest, it appears to have been insufficient. The South China Morning Post reports that the governing body, the Beijing Yanqing Cultural Commission, did not support the project as it "is not in line with the Great Wall's heritage conservation values".


Health Department Govt. Of India and CII(Confedration of Indian Industries) organised the 2012 AROGYA Fair in Bhubaneswar. The Fair is unique as Medicines and Health Awareness was created for the public. The Awareness Fair was on Homeopathy, Ayurveda, Unani, Sidhha and Naturopathy .
Stalls were presented with booklets, medicines and Knowledge Sorces.
For the three days Cultural Program-me were organised to Promote the Diversity of Odisha Culture, through Dance and Drama.


The Nova Scotia Duck Tolling Retriever is a small, enticing redhead with boundless energy. Right off the bat, he sounds appealing, but what's with that name? The Toller, as he's nicknamed, was developed in Nova Scotia's Little River district by hunters who wanted a dog who would attract birds as well as retrieve them. That's where the "tolling" comes in. The word refers to the dogs' habit of "dancing" on the shoreline, enticing curious ducks to come in for a closer look. Yes, it really works!
The Toller has a lot going for him: personality, size, versatility and an easy-care coat. But like any breed, he's not for everyone. Tollers are smart, independent and active. Their alert nature makes them excellent watchdogs. They love their own people but are reserved around strangers. Their size suits them to condo or apartment living, but only if you are equally smart and active and able to meet their needs for exercise, entertainment and consistent, patient training. The Toller is almost as intense as a Border Collie.
Channel his energy into dog sports such as agility, flyball and flying disc games, or teach him to pull you or your kid on skates or a skateboard.
Tollers take well to training, like most sporting breeds, but they are thinkers and they want to do things their own way. With this breed, it's important to establish rules, be consistent and, above all, prevent the dog from getting bored. Use positive reinforcement techniques such as play, praise and food rewards. When the motivation is there, the Toller learns quickly and easily.
Like most dogs, Tollers become bored when left to their own devices. They can easily become noisy or destructive if they don't have other dogs to keep them company and don't receive much attention from their people. But when the Toller lives with a family who is willing to spend plenty of time training and exercising him, he thrives.
The Toller is a wash-and-go dog. His medium-length, water-repellent double coat requires only weekly brushing to keep mats and tangles from forming. During spring and fall shedding seasons, daily brushing will help to keep excess hair under control. In addition, trim the nails as needed, clean and trim the foot pads, and keep the ears clean to prevent infections.
Last but not least, it should go without saying that a people-loving dog like the Toller needs to live in the house. It's an unhappy Toller who is relegated to the backyard with little or no human companionship.
Tollers have some health conditions that can be a concern, especially if you aren't cautious about whom you buy from. They include hip dysplasia, eye problems such as Collie eye anomaly and progressive retinal atrophy, Addison's disease and hypothyroidism. Because of his red coloring and self-colored nose, the Toller may also be predisposed to immune-mediated disease.
At a minimum ask the breeder to show evidence that both of a puppy's parents have hip scores of Excellent, Good or Fair from the Orthopedic Foundation for Animals and certification from the Canine Eye Registry Foundation that the eyes are healthy.
The Nova Scotia Duck Tolling Retriever Club (USA) participates in the Canine Health Information Center Program. For a Toller to achieve CHIC certification, he must have OFA certification or a PennHIP score for hips, eye clearance from the Canine Eye Registry Foundation and a DNA test for progressive retinal atrophy. Breeders must agree to have all test results, positive or negative, published in the CHIC database.
Find a good breeder who will match you with the right puppy, and will without question have done all the health certifications necessary to screen out health problems as much as possible. Start your search for a good breeder on the website of the Nova Scotia Duck Tolling Retriever Club (USA), and choose one who is committed to following the NSDTRC's Code of Ethics.
Consider an adult dog from a shelter or a rescue group. Many of the health problems in Tollers aren't apparent in puppyhood, but by adopting an older dog, most of them can be ruled out. In addition, Tollers can live 10 to 14 years or more, so an adult dog will still be a part of your family for a long time to come.
Puppy or adult, take your Toller to your veterinarian soon after adoption. Your veterinarian will be able to spot visible problems, and will work with you to set up a preventive regimen that will help you avoid many health issues.
Pet insurance for Nova Scotia Duck Tolling Retrievers costs more than for mixed breed dogs. This is because Nova Scotia Duck Tolling Retrievers are more likely than mixed breed dogs to make claims for hereditary conditions that are expensive to treat.
Embrace pet insurance plans offer full coverage for all breed-specific conditions (excluding those that are pre-existing) to which Nova Scotia Duck Tolling Retrievers are susceptible. The best time to get pet insurance for your Nova Scotia Duck Tolling Retriever is when he's a healthy puppy. You can't predict what will happen in the future, and pet insurance is the one thing you can't get when you need it the most.


Well, this is another article under game category and if you are owning android smartphone and game lover then you must came to know about Mini Militia. If you have not yet then here in this article you will know everything about this multi-player game.
Mini militia is one of the best multiplayer game you can play with friends during a boring lecture class or a get together. Maximum up to 12 players can be played in offline by using local Wi-Fi. Friends from different place can also connect to a common server and play the game in online to their heart contempt. During online play we need to have ESP points to buy pro pack like jet packs, bombs, dual wield ability for hand guns, and other rocket launcher, sniper rifle, laser, saw gun and lots more. Mini militia game has lots of maps to satisfy different user needs. And each map has a specialized gun of its own like in catacombs map the pro-gun is saw gun. Because we can get saw gun in this map up to 6 times it also has time settings we can change the time based on needs. Nitro packs and health packs are also available.
Lots of new updates too available like life indicator. When a person's life is below than half it will indicate so we can target a person with low life to gain points. For a newbie offline Training and Survival modes are available to improve their skills.
Mini Militia also comes with an in app purchase for upgrading to the pro pack. The mini militia pro pack provides some cool features like avatar customization, dual guns in online play, get rocket launcher, sniper and other guns in online play. Also one can use green bombs in the online play after resuming the play. Upgrading to pro pack only costs 1$ which is very affordable.
Few geeks over the internet have developed the custom mini militia mod apk which provides some unique features. These features are not available in the official game of mini militia. The mod apk version are created by few nerds out of interest. Some of the cool features of the mini militia mods are custom avatars, custom background music, HD background images, unlimited bombs, bullets and jet-pack.
The use control is simply awesome I had played this game for a quite long time but sill am not bored with the game. The user interface is great. Left control is for movement and right control is for aim and fire. On the top left zoom in and zoom out control followed by life and nitro indicator. Just to the top of the right control we can change the gun and bombs.
A player can hold up to 3 guns and maximum of dual gun at a time and all guns have different zoom in rate. While playing in local WIFI you don't require a pro pack for using advanced guns.
It also has boot ban option which helps very much when playing in online sometimes other players get connected to our custom group. At that time this option helps a lot.
Lots of mod apk available to specific hack such as for unlimited bullets. There is a apk available click here. Similarly lots of mod apk are available for different hacks such as unlimited smoke bomb, extra zoom and lots more.
The game play is pretty simple. Kill whoever comes in your way except when you are in teams. During multiplayer battle we can play in teams by guarding Each other backs. It has a custom server to host the online games. So two teams can war against other in online.
The main reason for the games success is the challenges between friends. The background score is simply awesome and the UI is designed like multi user interface. This also has one major advantage over other games. Here in this game both android and apple user can play together. Whereas apple user has a cutting edge in the game and in iPhone more number of layers can connect and has additional settings. Download this awesome game on your device and enjoy the play with your hostel-mates, roommates and classmates.


text:
Pappireddipatti
Ajjampatty - Dharmapuri
Ajjampatty Population - Dharmapuri, Tamil Nadu
Ajjampatty is a medium size village located in Pappireddipatti Taluka of Dharmapuri district, Tamil Nadu with total 254 families residing. The Ajjampatty village has population of 912 of which 452 are males while 460 are females as per Population Census 2011.
In Ajjampatty village population of children with age 0-6 is 79 which makes up 8.66 % of total population of village. Average Sex Ratio of Ajjampatty village is 1018 which is higher than Tamil Nadu state average of 996. Child Sex Ratio for the Ajjampatty as per census is 1135, higher than Tamil Nadu average of 943.
Ajjampatty village has lower literacy rate compared to Tamil Nadu. In 2011, literacy rate of Ajjampatty village was 58.94 % compared to 80.09 % of Tamil Nadu. In Ajjampatty Male literacy stands at 67.71 % while female literacy rate was 50.24 %.
As per constitution of India and Panchyati Raaj Act, Ajjampatty village is administrated by Sarpanch (Head of Village) who is elected representative of village. Our website, don't have information about schools and hospital in Ajjampatty village.
Ajjampatty Data
Total No. of Houses 254 - -
Population 912 452 460
Child (0-6) 79 37 42
Schedule Caste 2 0 2
Schedule Tribe 885 443 442
Total Workers 571 279 292
Main Worker 357 - -
Marginal Worker 214 106 108
Caste Factor
In Ajjampatty village, most of the village population is from Schedule Tribe (ST). Schedule Tribe (ST) constitutes 97.04 % while Schedule Caste (SC) were 0.22 % of total population in Ajjampatty village.
Work Profile
In Ajjampatty village out of total population, 571 were engaged in work activities. 62.52 % of workers describe their work as Main Work (Employment or Earning more than 6 Months) while 37.48 % were involved in Marginal activity providing livelihood for less than 6 months. Of 571 workers engaged in Main Work, 170 were cultivators (owner or co-owner) while 163 were Agricultural labourer.


I ran across this article by Herbal Republic, and it reminded me of the awakening I had when I switched from tea bags to loose leaf tea. It was like coming out of the shadows and into the sunlight.
" Loose leaf tea typically consists of larger, whole leaf material that better preserves its natural oils and fragrances than the dust found in most tea bags."
The difference is not subtle. The many teas (herbal and otherwise) available, make each cup an adventure into far off exotic lands, where a lot of these teas originate from.
"Loose leaf tea is valued worldwide for its ability to calm, re-energize, and reinvigorate the human body. Tea contains a delicate balance of chemicals that have the potential to make a huge positive impact on human health"
The Loose Leaf Teas that Naturiffic provides are blended with herbs designed to work in harmony to provide you with a great cup of tea, that will enchance your well being.
Worried about making the switch from Tea bags to Loose Leaf Tea? Don't be, just use our Strawberry Tea Infuser. It is made of food grade high temperature silicone and is easy to use and comes apart for cleaning. Its like a tea bag, except for loose leaf teas! Now you can have the best of both.


– Condolences are extended to the Rogers family on the recent passing of Packie in his 97th year. Ar dhes Dé go raibh a anam.
– Congratulations to Eamonn Murphy and Tara Tumilty on their recent marriage. Best wishes on your future life together.
– Congratulations to Eamonn and Gemma Moley on the birth of their baby boy, Rhys.
– Caoga: The Naomh Malachi Story documentary will go on sale in the month of November. Details of the launch night will appear in next week's newspaper editions.
– The staff and pupils celebrated a lovely Beginning of Year School Mass on September 28th. Thank you Fr. McKinney and to all who participated and attended.
– We had our school photos taken recently. All the pupils looked very smart, proof to follow soon!
– GAA training continues with Séan for the senior children. They are really enjoying it.
– We are looking forward to Maths Week, 17th – 21st October. Time to get those brains working!
– We have decorated our school with ghosts and goblins, pumpkins and witches!
– School closes for Halloween at 12.30pm on 28th October and reopen on 07th November.
– Condolences are extended to the Rogers family on the recent passing of Packie, Naomh Malachi Club President. Ar dhes Dé go raibh a anam.
– Congratulations to the Naomh Malachi/Kilkerley Emmets/St Mochtas U18 and U14 teams who both secured league silverware recently. Well done to all involved.
– Naomh Malachi Nominations night for our AGM will take place on Friday, November 4th in the clubrooms at 9pm. Please submit your nominations and/or motions to Eddie beforehand. Our AGM will take place on Sunday, November 20th at 6pm.
– Lotto 16-OCT: Jackpot €7500. Winning numbers: 4,11,12,13. No winner. Match x3 winner: Kevin Hamill c/o Packie Gogarty receives €100. Next lotto draw on 30-OCT. Thank you for your continued support.


MVD, the short form of the Spanish "movido", which means "emotionally moved", can be understood as the era of a cultural shift through the targeted use of visual media. MVD has set itself the goal of creating products and services through static as well as moving images and spaces, or the use of appropriate media. MVD sees itself as a team for the conception, development and realisation of visual media in a specific ambience. The spectrum ranges from classic corporate design projects in the fields of culture, art and research, to holistic interactive communication solutions in private and public spaces, to the implementation of own events in the discourse between popular culture and avant-garde.


Post IPO – Developed a first-time financial/cash/capital expenditure budget for a post-initial public offering developmental-stage company. Efforts included coordinating input from numerous reporting entities at multiple locations from differing disciplines to effect the development and production of the first budget.
Re-Engineering – Consulted and assisted in the development of an enhanced budgeting process. The engagement included a re-engineering of the budgeting process, development of timetables, coordination of efforts for numerous reporting entities at multiple locations, and consultation of the various responsible reporting parties.
Developmental-Stage SEC Client – Developed and produced a financial information package for a developmental-stage SEC client that was used to arrange for equipment/accounts receivable financing. The package included historical and current financial information, updated business plans for numerous entities, management organizational charts and biographies, and promotional materials. The engagement included the initial contact of potential financing entities, discussions about the company, and distribution of information. Work product resulted in the client obtaining a new financing facility for equipment purchases as well as accounts receivable financing.
Equity/Debt Financing – Consulted with a public company on various accounting issues relating to the accounting treatment of equity and debt financing and accounting consolidation. We assisted with the preparation of Forms 10-Q and commented on the preparation of Form S-3. We also analyzed and summarized the client's various equity transactions that occurred over an 18-month period.
First Time Through Audit – Assisted an international company in researching and developing information pursuant to a first time through audit. Work included investigation of transactions and reconstruction of records, and assistance to staff with accounting information subsequent to a complete physical move of manufacturing and warehousing facilities, and corporate and administrative functions.
External Audit Support – Provided audit support to many other Clients also.
Forensic Accounting – Provided forensic accounting for a major US metropolitan city regarding partnership in building of low-income housing; reconciled cash accounts back to job cost reports; assisted in the public audit for the Department of Housing; helped with the placing of bonds; general accounting review; and compiled documents for the initial audit of a municipal sports authority.
Risk Management – Assisted a start-up client with a variety of mid-office and back-office functions including: position keeping/reporting; commercial/gross margin accounting; contract administration; and counter-party credit policies, controls and analysis. Additional assistance included working with a Big Four accounting firm's risk management advisory group, participating in developing overall control and risk strategy, assisting in developing policies, procedures and systems for the business activities, and systems selection and implementation.
Provided numerous Clients post acquisition assistance which included clean-up of accounts through detailed reconciliations, push-down accounting entries, M&A entries, tax compliance review, tax strategies, and inventory re-location assistance.
Assisted Clients with various systems needs including implementation of a multi-location ERP system, a financial reporting infrastructure and a forecasting system.
Audit Methodology – Assisted a Fortune 500 client with the design and development of an audit methodology that included audit mandates and plans for the client's worldwide internal audit function. Our efforts resulted in the production of an internal audit policies and procedures manual as well as providing introductory training.
Training – Organized and directed a course development engagement to provide our client with formalized introductory training for the client's worldwide internal audit function. The engagement included conducting the course in US and international locations.
Internal Audit Support – Provided internal audit support to variety of Clients in different industries, including many Sarbanes-Oxley Specialists to augment internal audit functions.
Provided Clients with an array of tax services, including year-end tax compliance support and multi-state SALT analysis and resolution projects.
Provided Clients with an array of interim financial management, including Controllers, Assistant Controllers, and Financial Reporting Managers.


In every person is an innate championship status. Most persons though do not find it because either they are too busy or they actually do not know how. This book will stimulate you and drive you to that endpoint.


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_Geography Notes
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Gk Quiz on Election Commission of India (ECI) Quiz on Election Commission of India (ECI)
Quiz on Election Commission of India (ECI)
by GovtJobNotes on September 11, 2021 in Gk, Quiz on Election Commission of India (ECI)
Take the GK Quiz based on Election Commission of India below. Answer the questions diligently.
Election Commission of India Quiz
The Election Commission of India (ECI) is an autonomous constitutional authority responsible for the Union and State election processes in India. Take the quiz based on the Election Commission of India here.
Que.1). Who was the first Election Commissioner of India?
a). Sukumar Sen
b). VS Ramadevi
c). Shankar Dayal Sharma
d). Rajendra Prasad
Ans. a
Que.2). Which article of the Indian Constitution states that there would be an Election Commission to control, direct elections in India
a). Article 344
b). Article 324
c). Article 333
d). Article 370
Ans. b
Que.3). Choose the correct statement about Election Commission
i) It was a single member body
ii) The commission consists of one Chief Election Commissioner and two Election Commissioners
iii) Earlier there was only one election commissioner but after Election Commissioner Amendment Act 1989, it became a multi member body.
a). i and ii
b). ii and iii
c). i, ii and iii
d). None of the above
Que.4). Where is the Secretariat of the Election Commission located?
a). New Delhi
b). Mumbai
c). Gurgaon
d). Chandigarh
Que.5). Who appoints the Election Commissioners?
a). Chief Election Commissioner
b). President of India
c). Prime Minister of India
d). Parliament of India
Que.6). What is the tenure of the Election Commissioner of India?
a). 5 years or 65 years of age, whichever earlier
b). 6 years or 65 years of age, whichever earlier
c). 4 years or 63 years, whichever earlier
d). 5 years or 62 years, whichever earlier
Que.7). Which of the following is not correct about the Election Commissioners of India?
i) They enjoy the same status as Judges of the Supreme Court of India
ii) CEC may be removed from office through a motion adopted by Parliament on grounds of proved misbehaviour or incapacity.
a). Only i
b). Only ii
c). Both i and ii
Ans. d
Que.8). What is the majority required to remove the Chief Election Commissioner of India?
i) Special majority of 2/3rd members present and voting
ii) More than 50% of the strength of the house
iii) 2/3 of the total strength of the Lok Sabha
b). i and iii
c). ii and iii
d). i, ii, iii
Que.9). Election Commission is a
a). Constitutional Body
b). Statutory Body
c). Elective Body
Que.10). Who is the current Election Commissioner of India
a). Sushil Chandra
b). Sunil Arora
c). Om Prakash Rawat
Recommended : Quiz on Interstate Migrant Workmen Act, 1979
By GovtJobNotes at September 11, 2021
Labels: Gk, Quiz on Election Commission of India (ECI)
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Move in ready upper lever condo close to OU. 3 bed, 2 bath, right across from the pool with a balcony to enjoy your morning coffee, a spacious storage unit with shelvings, and a staircase asses to the back of the complex. HOA fees cover water, trash, exterior maintenance, insurance, community pool and laundry.
Offered by Mark Mccurdy of Don Cies Real Estate, Inc.
Listing provided courtesy of Mark Mccurdy of Don Cies Real Estate, Inc.