text:
\section{Introduction}
Sentence summarization
transforms a long source sentence into a short summary, while preserving key information~\citep{rush2015neural}. Sentence summarization has wide applications, for example, news headline generation and text simplification.

State-of-the-art sentence summarization systems are based on sequence-to-sequence neural networks~\cite{rush2015neural, nallapati2016abstractive, wang-etal-2019-biset}, which
require massive parallel data for training.
Therefore, unsupervised sentence summarization has recently attracted increasing interest. Cycle-consistency approaches treat the summary as a discrete latent variable and use it to reconstruct the source sentence \citep{wang-lee-2018-learning, baziotis2019seq}. Such latent-space generation fails to explicitly model the resemblance between the source sentence and the target summary. \citet{zhou2019simple} propose a left-to-right beam search approach based on a heuristically defined scoring function. However, beam search is biased towards the first few words of the source.
\begin{figure}[t]
    \centering
    \includegraphics[width=0.47\textwidth]{figures/binary_progress.pdf}
    \caption{Summarizing a sentence $\mathbf{x}$ by hill climbing. Each row is a Boolean vector $\bm{a}_t$ at a search step $t$ . A black cell indicates a word is selected, and \textit{vice versa}. Randomly swapping two values in the Boolean vector yields a new summary that is scored by an objective function that measures language fluency and semantic similarity. If the new summary increases the objective, this summary is accepted as the current best solution. Rejected solutions are not depicted.}
    \label{fig:progress}
\end{figure}

In this paper, we propose a hill-climbing approach to unsupervised sentence summarization, directly extracting words from the source sentence. This is motivated by the observation that human-written reference summaries exhibit high word overlap with the source sentence, even preserving word order to a large extent.
To perform word extraction for summarization, we define a scoring function --- similar to \newcite{miao2019cgmh} and \newcite{zhou2019simple} --- that evaluates the quality of a candidate summary by language fluency, semantic similarity to the source, and a hard constraint on output length. We search towards our scoring function by first choice hill-climbing (FCHC), shown in Figure~\ref{fig:progress}. We start from a random subset of words of the required output length. For each search step, a new candidate is sampled by randomly swapping a selected word and a non-selected word. We accept the new candidate if its score is higher than the current one. In contrast to beam search~\cite{zhou2019simple}, our summary is not generated sequentially from the beginning of a sentence, and therefore not biased towards the first few words.

Due to the nature of the search action, our approach is able to \textit{explicitly} control the length of a summary as a hard constraint. In all previous work, the summary length is weakly controlled by length embeddings or a soft length penalty \cite{zhou2019simple,wang-lee-2018-learning,fevry2018unsupervised,baziotis2019seq}.
Thus, the generated summaries by different systems vary considerably in average length, for example, ranging from 9 to 15 on a headline corpus (Section~\ref{sec:data}). Previous work uses \Rouge{} F1 to compare summaries that might differ in length. We show
that \Rouge{} F1 is unfortunately sensitive to summary output length, in general favoring models that produce longer summaries. Therefore, we argue that controlling the output length should be an integral part of the summarization task and that a fair system comparison can only be conducted between summaries in the same length bracket.

Our model establishes a new state-of-the-art for unsupervised sentence summarization across all commonly-used length brackets and different \Rouge{} metrics on the Gigaword dataset for headline generation \cite{rush2015neural} and on DUC2004 \cite{over2004introduction}.

The main contributions of this paper are:
\begin{compactitem}[$\bullet$]
\item We propose a novel method for unsupervised sentence summarization by hill climbing with word-level extraction.
\item We outperform current unsupervised sentence summarization systems, including more complex sentence reconstruction models.
\item We show that \Rouge\ F1 is sensitive to summary length and thus emphasize the importance of explicitly controlling summary length for a fair comparison among different summarization systems.
\end{compactitem}
\section{Related Work}
\label{sec:related_work}

\textbf{Text Summarization.}
The task can be categorized by source text types, such as \textit{multi-document summarization}~\cite{erkan,radev-etal-2000-centroid,haghighi} and \textit{single-document summarization}~\cite{mihalcea-tarau-2004-textrank, zhou-hovy-2004-template, zheng-lapata-2019-sentence}.
Traditional approaches are mostly \textit{extractive}, i.e., they extract entire sentences from a document. Recently, sequence-to-sequence (Seq2Seq) models have been used for \textit{abstractive} summaries, where the system is able to synthesize new sentences \cite{nallapati2016abstractive, nallapati2017summarunner, gehrmann2018bottom, lewis2019bart,fabbri2019multi}. The copy mechanism~\cite{gu2016incorporating} in a Seq2Seq model can be viewed as word-level extraction in abstractive summarization \cite{see2017get, paulus2017deep}. Both state-of-the-art extractive and abstractive approaches are usually supervised.

\textit{Sentence summarization} yields a short summary for a long sentence. \citet{hori2004speech} and \citet{clarke2006constraint} extract single words from the source sentence based on language model fluency and linguistic constraints. They search via dynamic programming with a trigram language model, which restricts the model capacity.
The Hedge Trimmer method \cite{dorr2003hedge} also uses hand-crafted linguistic rules to remove constituents from a parse tree until a certain length is reached.

\citet{rush2015neural} propose a supervised abstractive sentence summarization system with an attention mechanism \citep{bahdanau2014neural}, and they also introduce a dataset for headline generation derived from Gigaword.\footnote{\url{https://catalog.ldc.upenn.edu/LDC2003T05}} Subsequent models for this dataset were also supervised and mostly based on Seq2seq architectures \cite{nallapati2016abstractive, chopra2016abstractive, wang-etal-2019-biset}.

Recently, unsupervised approaches for sentence summarization have attracted increasing attention. \citet{fevry2018unsupervised} learn a denoising autoencoder and control the summary length by a length embedding. \citet{wang-lee-2018-learning} and \citet{baziotis2019seq} use cycle-consistency \cite{NIPS20166469} to learn the reconstruction of the source sentence and return the intermediate discrete representation as a summary. \citet{zhou2019simple} use beam search to optimize a scoring function, which considers language fluency and contextual matching.

Our work can be categorized under unsupervised sentence summarization. We accomplish this by word-level extraction from the source sentence.

\textbf{Constrained Sentence Generation.}
Neural sentence generation is usually accomplished in an autoregressive way, for example, by recurrent neural networks generating words left-to-right. This is often enhanced by beam search \cite{sutskever2014sequence}, which keeps a beam of candidates in a partially greedy fashion.
A few studies allow hard constraints on this decoding procedure. \citet{hokamp2017lexically} use grid-beam search to impose lexical constraints during decoding. \citet{anderson-etal-2017-guided} propose constrained beam search to predict fixed image tags in an image transcription task. \citet{miao2019cgmh} propose a Metropolis--Hastings sampler for sentence generation, where hard constraints can be incorporated into the target distribution. This is further extended to simulated annealing~\cite{UPSA}, or applied to the text simplification task~\cite{simplification}. Different from the above concurrent work, this paper applies the stochastic search framework to text summarization, and design our specific search space and search actions for word extraction.

In previous work on text summarization, length embeddings~\cite{kikuchi-etal-2016-controlling,fan2018controllable} have been used to indicate the desired summary length. However, these are not hard constraints, because the model may learn to ignore such information.

\section{Proposed Model}
Given a source sentence $\mathbf{x}=(x_1, x_2, \dots, x_n)$ as input, our goal is to generate a shorter sentence $\mathbf{y}=(y_1, y_2, \dots, y_m)$ as a summary of $\mathbf{x}$. We perform word-level extraction, in addition keeping the original word order intact. Thus, $\mathbf{y}$ is a subsequence of $\mathbf x$. Our word-level extraction optimizes a manually defined objective function $f(\mathbf{y};\mathbf{x}, s)$, where the summary length $s$ is predefined ($s<n$) and not subject to optimization. In the remainder of this section, we will describe the objective function, search space, and the search algorithm in detail.
\subsection{Search Objective}

We define an objective function $f(\mathbf y;\mathbf x, s)$, which our algorithm maximizes. It evaluates the fitness of a candidate sentence $\mathbf{y}$ as the summary of an input $\mathbf{x}$, involving three aspects, namely, language fluency $f_{\overleftrightarrow{\mathrm{LM}}}(\mathbf{y})$, semantic similarity $f_{\mathrm{SIM}}(\mathbf{y};\mathbf{x})$, and a length constraint $f_{\mathrm{LEN}}(\mathbf{y}, s)$. This is given by

\small
\begin{equation}
f(\mathbf y;\mathbf x,s)=f_{\overleftrightarrow{\mathrm{LM}}}(\mathbf{y})\cdot f_{\mathrm{SIM}}(\mathbf{y};\mathbf{x})^\gamma \cdot f_{\mathrm{LEN}}(\mathbf{y}; s),
\end{equation}

\vspace{.2cm}
\normalsize\noindent where the relative weight $\gamma$ balances $f_{\overleftrightarrow{\mathrm{LM}}}(\mathbf{y})$ and $f_{\mathrm{SIM}}(\mathbf{y};\mathbf{x})$. We treat the summary length as a hard constraint, and therefore we do not need a weighting hyperparameter for $f_{\mathrm{LEN}}$.

\textbf{Language Fluency.}
The language fluency scorer quantifies how grammatical and idiomatic a candidate summary $\mathbf{y}$ is. Our model generates a candidate summary in a non-autoregressive fashion, in contrast to the beam search in \citet{zhou2019simple}. Thus, we are able to simultaneously consider forward and backward language models, using the geometric average of their perplexities. Using both forward and backward language models is less biased towards sentence beginnings or endings.\vspace{-.3cm}

\small
\begin{equation}\nonumber
\overleftrightarrow{\mathrm{PPL}}(\mathbf{y})=\sqrt[\leftroot{-2}\uproot{2}2|\mathbf{y}|]{\prod_{i}^{|\mathbf{y}|} \frac{1}{p_{\overrightarrow{\mathrm{LM}}}(y_i|\mathbf{y}_{<i})}\prod_{i}^{|\mathbf{y}|} \frac{1}{p_{\overleftarrow{\mathrm{LM}}}(y_i|\mathbf{y}_{>i})}}.
\end{equation}
\normalsize Our fluency scorer is the inverse perplexity.

\small\begin{equation}
f_{\overleftrightarrow{\mathrm{LM}}}(\mathbf{y})={\overleftrightarrow{\mathrm{PPL}}(\mathbf{y})}^{-1}.
\end{equation}

\normalsize Depending on applications, the language models could be pretrained on a target corpus.\footnote{We use the terminology \textit{unsupervised summarization}, following \citet{zhou2019simple}. While we train the language models on the desired target language, we do not need parallel source-target pairs, i.e., sentences together with their groundtruth summaries.} In this case, the fluency scorer also measures
whether the summary style is consistent with the target language.
This could be important in certain applications, e.g., headline generation, where the summary language differs from the input in style.

\textbf{Semantic Similarity.}
A semantic similarity scorer ensures that the summary keeps the key information of the input sentence. We adopt the cosine similarity between sentence embeddings as\vspace{-.2cm}

\small\begin{equation}
f_{\mathrm{SIM}}(\mathbf{y}; \mathbf{x})=\cos(\bm e(\mathbf{x}), \bm e(\mathbf{y})),
\end{equation}
\normalsize where $\bm e$ is a sentence embedding method. In our work, we use unigram word embeddings learned by the sent2vec model \cite{pagliardini-etal-2018-unsupervised}.
Then, $\bm{e}(\mathbf x)$ is computed as the average of these unigram embeddings, weighted by the inverse-document frequency (\textit{idf}) of the words.

We use sent2vec because it is trained in an unsupervised way on individual sentences. By contrast, other unsupervised methods like SiameseCBOW \cite{kenter-etal-2016-siamese} or BERT \cite{devlin-etal-2019-bert} use adjacent sentences as part of the training signal.

\textbf{Length Constraint.} \label{sss:length}
Our discrete searching approach is able to impose the output length as a hard constraint, allowing the model to generate summaries of any given length.
Suppose the desired output length is $s$, then our length scorer is\vspace{-.2cm}

\small\begin{equation}
f_{\mathrm{LEN}}(\mathbf{y}; s)=
\begin{cases}
  1, & \text{if}\ |\textbf{y}|=s, \\
  -\infty, & \text{otherwise}.
\end{cases}
\end{equation}
\normalsize In other words, a candidate summary $\mathbf y$ is \textit{infeasible} if it does not satisfy the length constraint. In practice, we implement this hard constraint by searching among \textit{feasible solutions} only.

\subsection{Search Space}

Most sentence generation models choose a word from the vocabulary at each time step, such as autoregressive generation that predicts the next word~\cite{sutskever2014sequence,rush2015neural}, and edit-based generation with deletion or insertion operations~\cite{miao2019cgmh,dong}. In these cases, the search space is $|\mathcal{V}|^s$, given a vocabulary $\mathcal{V}$ and a summary length $s$.

However, reference summaries are highly extractive. In the headline generation dataset~\cite{rush2015neural}, for example, 45\% of the words in the reference summary also appear in the source sentence. This yields a ceiling of 45 \mbox{\Rouge{}-1} F1 points\footnote{We assume an extracted summary has the same length as the reference, and 45\% words of the reference are in the original sentence. This gives us a ceiling of 45\% precision and recall.} for a purely extractive method, which is higher than the current state-of-the-art supervised abstractive result of 39~points~\cite{wang-etal-2019-biset}.
We are thus motivated to propose our word-extraction approach that extracts a subsequence of the input as the summary.
 Additionally, we arrange the words in the same order as the input, motivated by the \textit{monotonicity assumption} in summarization~\citep{yu2016online, raffel2017online}.

Formally, we define the search space as $\bm a = (a_1, \dots, a_n) \in\{0,1\}^n$, where $n$ is the length of the input sentence $\mathbf x$. The vector $\bm a$ is a Boolean filter over the source words $\mathbf{x}$. The summary sequence can then be represented by $\mathbf{y}=\mathbf{x}_{\bm a}$, i.e., we sequentially extract words from the source sequence $\mathbf {x}$ by the Boolean vector $\bm a$. If $a_i=1$, then $x_i$ is extracted for the summary, and {vice versa}.

Further, we only consider the search space of all feasible solutions $\{\bm a:f(\mathbf x_{\bm a};\mathbf x, s) > -\infty$\}. That is to say, the candidate summary has to satisfy the length constraint in Section~\ref{sss:length}. Equivalently, the output length can be expressed by a constraint on the search space such that $\sum_i a_i=s$.

The above restrictions reduce the search space to $\binom{n}{s}$ solutions. In a realistic setting, our search space is much smaller than that of generating words from the entire vocabulary.

\small\input{figures/algorithm.tex}
\normalsize\subsection{Search Algorithm}
We optimize our objective function $f(\mathbf{y};\mathbf{x}, s)$ by first-choice hill climbing \cite[FCHC,][]{russell2016artificial}. This is a stochastic optimization algorithm that proposes a candidate solution by local change at every search step. The candidate is accepted if it is better than the current solution. Otherwise, the algorithm keeps the current solution. FCHC maximizes the objective function in a greedy fashion and yields a (possibly local) optimum.

Algorithm~\ref{algorithm:shc} shows the optimization procedure of our FCHC. For each search step, a new candidate is sampled from the
neighbor function $q(\bm a'|\bm a)$.
This is accomplished by randomly swapping two actions $a_i$ and $a_j$ for $a_i\ne a_j$, i.e., replacing a word in the summary with a word from the source sentence that is not in the current summary. The order of selected words is kept
as in the source sentence.
If the candidate solution achieves a higher score, then it is accepted. Otherwise, the candidate is rejected and the algorithm proceeds with the current solution. Our search terminates if it exceeds a pre-defined budget. The last solution is returned as the summary, as it is also the best-scored candidate due to our greedy algorithm.

One main potential drawback of hill climbing algorithms is that they may get stuck in a local optimum. To alleviate this problem, we restart the algorithm with multiple random initial word selections $\bm a_0$ and return the overall best solution. We set the number of restarts as \mbox{$\beta_R\cdot ns^2$} and number of search steps as \mbox{$\beta_T\cdot ns^2$}, where $\beta_R$ and $\beta_T$ are controlling hyperparameters. We design the formula to encourage more search for longer input sentences, but only with a tractable growth: linear for input length and quadratic for summary length. As the summary length is usually much smaller than the input length, quadratic search is possible. Increasing the number of restarts (and search steps) monotonically improves the scoring function, and thus in practice can be set according to the available search budget.

Other discrete optimization algorithms can be explored for sentence generation, such as simulated annealing~\cite{UPSA} and genetic algorithms. Our analysis on short sentences (where exhaustive search is tractable) showed that hill climbing with restarts achieves \Rouge{} scores similar to exhaustive search (Section~\ref{sec:analysis}).

\section{Evaluation Framework}
\label{sec:evaluation}
In this section, we will describe the datasets, evaluation metrics, and a widely used baseline (called Lead).
Additionally, we report the observation that the commonly used evaluation metric, \Rouge{} F1, is sensitive to summary length, preferring longer summaries. Thus, we propose to group models with similar output length during evaluation for fair comparison.

\subsection{Datasets}\label{sec:data}
We evaluate our models on the dataset provided for DUC2004 Task 1 \citep{over2004introduction} and a headline generation corpus\footnote{\url{https://github.com/harvardnlp/NAMAS}} \cite{rush2015neural}, both widely adopted in the summarization literature.

The DUC2004 dataset is designed and used for testing only. It consists of 500 news articles, each paired with four human written summaries. We follow \citet{rush2015neural} and adopt DUC2004 for sentence summarization by using only the first sentence of an article as input. The reference summaries are around 10 words long on average.

The headline generation dataset \cite{rush2015neural} is derived from the Gigaword news corpus. Each headline/title is viewed as the reference summary of the first sentence of an article. The dataset contains 3.8M training instances and 1951 test instances. The average headline contains $\sim$8 words; the average source sentence contains $\sim$30 words. We use 500 held-out validation instances for hyperparameter tuning. Note that the training set is only used to train a language model and sent2vec embeddings. The summarization process itself is not trained in our approach.

\subsection{Lead Baselines}\label{ss:lead}
Lead baselines are a strong competitor that extracts the first few characters or words of the input sentence.
The DUC2004 shared task includes a Lead baseline, which extracts the first 75 characters as the summary. We call it Lead-C-75. For the Gigaword dataset, the reference has 8 words on average, and it is common to compare with a Lead variant that chooses the first 8 words. We call this baseline Lead-N-$n$ when we choose $n$ words.
For fair comparison with previous work \cite{baziotis2019seq,fevry2018unsupervised} in Section~\ref{sec:competing_models}, we further introduce a new variant that returns the first $p$ percent of source words as the summary. We denote this baseline by {{Lead-P}-$p$}.

\subsection{\Rouge{} Scores}

Summarization systems are commonly evaluated by \Rouge{} scores \citep{lin2004rouge}. The \Rouge{}-1 (or \Rouge{}-2) score computes the unigram (or bigram) overlap of a generated summary and the reference. \Rouge{}-L calculates the longest common subsequence. Depending on the dataset, either \Rouge{} Recall or \Rouge{} F1 variant is adopted. Since the \Rouge{} Recall metric is not normalized with regard to length, DUC2004 standard evaluation truncates the summary at 75 characters. This procedure was also adopted by \citet{rush2015neural} for the headline generation task, but later \citet{chopra2016abstractive} proposed to report the ``more balanced" \Rouge{} F1 metric for the Gigaword headline generation dataset and abandoned truncation. We follow previous work and use {{\Rouge{} F1} for headline generation and \mbox{{truncated \Rouge{} Recall}} for DUC2004}.

\subsection{Summary Length}\label{section:sumary_length}
As mentioned, \Rouge{} F1 was introduced to the evaluation of sentence summarization to better compare models with different output lengths~\cite{chopra2016abstractive, nallapati2016abstractive}. To investigate the effect of summary length on \Rouge{} F1, we calculate \Rouge{} F1 scores for the Lead-N-$n$ and Lead-P-$p$ baselines with different length parameters. Figure~\ref{figure:leadn_leadp} shows that \Rouge{} F1 peaks at $n\approx18$ or $p\approx50$. The difference between the maximum performance at $n\approx18$ and the widely adopted baseline (Lead-N-$8$) is large: 4.2 \Rouge-1 F1 points.
A similar effect is observed by \citet{sun-etal-2019-compare} for document summarization. This shows that \Rouge{} F1 is still sensitive to summary length, and this effect should be considered during evaluation. We propose to report the average output length of a model and only compare models in the same length bracket.
\begin{figure}[!t]
  \centering
     \begin{subfigure}{0.49\textwidth}
  \includegraphics[width=\textwidth]{figures/lead_n_baseline_f1.pdf}
  \label{fig:lead_n_f1}
  \end{subfigure}
  \hfill
  \vspace*{-0.7cm}
  \begin{subfigure}{0.49\textwidth}
  \includegraphics[width=\textwidth]{figures/lead_p_baseline_f1.pdf}
  \label{fig:lead_p_f1}
  \end{subfigure}
\vspace{-0.7cm}
\caption{\Rouge{} F1 scores on the test set of headline generation for Lead-N and Lead-P baselines with different number $n$ and percentage $p$ of leading words.}
\label{figure:leadn_leadp}
\end{figure}

\section{Experiments}

\subsection{Setup}

We conduct experiments with two settings, dependent on how the scorers $f_{\overleftrightarrow{\mathrm{LM}}}$ and $f_{\mathrm{SIM}}$ are trained.
In the first setting, we train the language model and sent2vec embeddings on the \textbf{source} (article) side of the Gigaword headline generation dataset. This complies with \newcite{fevry2018unsupervised} and \newcite{baziotis2019seq}.
In the second setting, we train the language model and sent2vec embeddings on the \textbf{target} (title) side like \citet{zhou2019simple}. In both settings, we do not need parallel source-target pairs.

For output length, our headline generation experiment sets the desired target length as 8 words, 10 words, and 50\% of the input, as these mirror
either the average reference summary length or the average output lengths of our competitors~\cite{wang-lee-2018-learning,zhou2019simple,fevry2018unsupervised,baziotis2019seq}.
For DUC2004, the desired summary length is set to 13 words, because the standard evaluation script truncates after the first 75 characters (roughly 13 words) in the summary.

Our forward and backward language models use long short term memory units \citep{hochreiter1997long} and are optimized for 50 epochs by stochastic gradient descent. Embeddings and hidden sizes are set to 1024 dimensions.

We tune hyperparameters on the development data of the headline corpus, and set the weighting parameter $\gamma$ to 12 for all models. The search steps and restarts are set to \mbox{$\beta_T=0.1$} and \mbox{$\beta_R=0.035$}, respectively. We see a sharp performance improvement when we do more searching. Thus, we choose $\beta_T$ and $\beta_R$ at the critical values due to efficiency concerns.

\input{tables/ext_giga.tex}

\input{tables/ext_DUC2004.tex}

\subsection{Competing Models}
\label{sec:competing_models}
Besides the {Lead} baselines discussed in Section~\ref{ss:lead}, we compare our models with state-of-the-art unsupervised sentence summarization systems.

\citet{wang-lee-2018-learning}\footnote{Generated summaries are obtained via E-Mail correspondence. Scores differ because of evaluation setup.} use cycle-consistency to reconstruct source sentences from the headline generation corpus \cite{rush2015neural}. The latent discrete representation, learned to be similar to (non-parallel) headlines, is used as the summary.

\citet{zhou2019simple} optimize an objective function involving language fluency and contextual matching. Their language modeling scorer is trained on headlines of the Gigaword training set; their contextual matching scorer is based on ELMo embeddings \cite{peters-etal-2018-deep} trained with the Billion Word corpus \citep{chelba2013one}. Their summary length is controlled by a soft length penalty during beam search.

\citet{fevry2018unsupervised}\footnote{Retrained with official code (\url{https://github.com/zphang/usc_dae}) because the authors use a private test set.} learn a denoising autoencoder \cite{vincent2008extracting} to reconstruct source sentences of the Gigaword training set. Summary length is set to 50\% of the input length and is controlled by length embeddings in the decoder.

\citet{baziotis2019seq}\footnote{Retrained with official code (\url{https://github.com/cbaziotis/seq3}), because of different test data. The authors remove 54 noisy instances. Our replication thus achieves slightly lower scores than theirs.} propose \texttt{SEQ$^3$} that uses cycle-consistency to reconstruct source sentences from the Gigaword training set. The length is also set to 50\% of the input length, controlled by length embeddings in the intermediate decoder.

For the DUC2004 dataset, \textsc{Topiary} \cite{topiary} is the winning system in the competition. They shorten the sentence by rule-based syntax-tree trimming \cite{dorr2003hedge}, but enhance the resulting summary with topics that are learned on full articles.

\textsc{BottleSum Ex} \cite{west-etal-2019-bottlesum} uses the information bottleneck principle to predict the next sentence in an article. Their method employs a pretrained {small} GPT-2 model \cite{radford2019language}.

\subsection{Results}

\textbf{Results for Headline Generation.}
We first compare with Lead-N-8 (Group A, Table~\ref{table:ext_giga}). This is a standard baseline in previous work, because the average reference summary contains eight words. Unfortunately, none of the previous papers consider output length during evaluation, making comparisons between their (longer) output summaries and the Lead-N-8 baseline unfair, as discussed in Section~\ref{section:sumary_length}. Our approach, which explicitly controls summary length, considerably outperforms the Lead-N-8 baseline in a fair setting.

Next, we compare with state-of-the-art unsupervised methods, whose output summary has roughly 10 words on average (Group B). In this case, we set our hard length constraint as 10 and include the Lead-N-10 baseline for comparison.
Trained on the title side only, our \textit{HC\_title\_10} model outperforms these competing methods in all \Rouge{} F1 scores. In particular, \newcite{zhou2019simple} use the target side to train the language model, plus the Billion Word Corpus to pretrain embeddings used in the contextual matching scorer. With the same extra corpus to pretrain our sent2vec embeddings, our \textit{HC\_title+billion\_10} variant achieves even better performance, outperforming \citet{zhou2019simple} by 2.32 \Rouge-1 and 1.41 \Rouge-L points.

The Billion Word Corpus, however, includes complete articles, which implicitly yields unaligned parallel data. This could be inappropriate for an unsupervised method. Thus, we further train sent2vec embeddings on the Twitter corpus by \citet{pagliardini-etal-2018-unsupervised}.
The \textit{HC\_title+twitter\_10} also performs better than \textit{HC\_title\_10} and other competitors.

In Group C, we compare with the models whose summaries have an average length of 50\% of the input sentence. We set our desired target length to 50\% as well, and include the Lead-P-50 baseline. Previous studies report a performance improvement over the {Lead-N-8} baseline, but in fact, Table~\ref{table:ext_giga} shows that they do not outperform the appropriate Lead baseline Lead-P-50. Our model is the only unsupervised summarization system that outperforms the Lead-P-50 baseline on this dataset, even though it is trained solely on the article side.

It is noted that our models trained on the title side (\textit{HC\_title}) consistently outperform those trained on the article side (\textit{HC\_article}). This is not surprising because the former can generate headlines from the learned target distribution. This shows the importance of learning a summary language model even if we do not have supervision of parallel source-target data.

\textbf{Results for DUC2004.}
Table~\ref{table:ext_DUC2004} shows the results on the DUC2004 data. As this dataset is for test only, we directly transfer the models \textit{HC\_article} and \textit{HC\_title} from the headline generation corpus with the same hyperparameters (except for length). As shown in the table, we outperform all previous methods and the Lead-C-75 baseline. The results are consistent with Table~\ref{table:ext_giga}, showing the generalizability of our approach.

\textbf{Human Evaluation.} We conduct human evaluation via pairwise comparison of system outputs, in the same vein as \cite{west-etal-2019-bottlesum}. The annotator sees the source sentence along with the headline generated by our system and a competing method, presented in random order. The annotator is asked to compare the fidelity and fluency of the two systems, choosing among the three options (i) the first headline is better (ii) the second headline is better, and (iii) both headlines are equally good/bad. This task is repeated for 100 instances with 5 annotators each. The final label is selected by majority voting. The inter-annotator agreement (Krippendorff's alpha) is 0.25 when our model is compared with \newcite{wang-lee-2018-learning} and 0.17 with~\newcite{zhou2019simple}.

We report the aggregated score of our system in Table~\ref{fig:human}. For each sample, we count 1 point if our model wins, 0 points if it ties, -1 point if it loses. The points are normalized by the number of samples. The results show an advantage of our model over \newcite{wang-lee-2018-learning}, especially in fluency. Our model is also on par with \citet{zhou2019simple}. Note again that we achieve this with fewer data.

\input{tables/human_evaluation.tex}
\subsection{Analysis}\label{sec:analysis}
In this section, we conduct  an in-depth analysis of our model, based on \textit{HC\_title\_10} for headline generation.

\input{tables/ablation.tex}

\textbf{Search Objective.} Table~\ref{table:ablation} provides an ablation study on our objective function. It shows that both language fluency and semantic similarity play a role in measuring the quality of a summary. The bi-directional language model is also slightly better than a uni-directional language model.

\textbf{Search Algorithm.} In Figure~\ref{fig:exhaustive}, we compare our FCHC with the theoretical optimum on short sentences where exhaustive search is tractable. For only 3\% of the instances with source sentence length between 25 and 30 words, our FCHC algorithm does not find the global optimum. In 21\% of those cases, the better objective score leads to a higher \Rouge-L score. This shows that FCHC with restarts is a powerful enough search algorithm for word extraction-based sentence summarization.
\begin{figure}[t]
    \centering
    \vspace{-.2cm}
    \includegraphics[width=0.49\textwidth]{figures/exhaustive.pdf}
    \caption{Orange crosses show the objective score optimized by exhaustive search minus the objective score optimized by FCHC. Blue pluses show the \Rouge-L difference between exhaustive search and FCHC. Plotted for the 1135 instances in the headline generation test set, where the source sentence has 30 words or fewer.}
    \label{fig:exhaustive}
\end{figure}

\textbf{Positional Bias.}
\begin{figure}[t]
    \centering
    \vspace{-.2cm}
    \includegraphics[width=0.49\textwidth]{figures/extract_position.pdf}
    \caption{Positional bias for different systems, calculated for the headline generation test set. The source sentence is divided into four areas: 0--25\%, 25--50\%, 50--75\%, and 75-100\% of the sentence. The $y$-axis shows the normalized frequency of how often a word in the summary is extracted from one of the four source sentence areas.}
    \label{fig:position}
\end{figure}
We analyze the positional bias of each algorithm by plotting the normalized frequency of extracted words within four different areas of the source sentence. As shown in Figure \ref{fig:position}, the extraction positions of words in the reference headlines are slightly skewed towards the beginning of the source sentence. Our hill-climbing algorithm performs distributed edits over the sentence, which is reflected in the flatter graph across the source sentence areas. By contrast, beam search \cite{zhou2019simple} is more biased towards the first quarter of the source sentence. Cycle consistency models \cite{wang-lee-2018-learning, baziotis2019seq} show a strong bias towards the first half of the source sentence. We suspect that the reconstruction decoder is easily satisfied with the beginning of the source sentence as the discrete latent variable, because of its autoregressive decoding.

\textbf{Case Study.}
We show example summaries generated by our system in Figure \ref{fig:examples}. We see that the \textit{HC\_title} models indeed learn the style of headlines, known as \textit{headlinese}. As shown, \textit{HC\_title} often uses simple tense and drops articles (e.g., ``a'' and ``the''). The summaries generated by \textit{HC\_article} tend to waste word slots by including an uninformative determiner.

It is also seen that we can control the length in an explicit way. Comparing \textit{HC\_title} with desired lengths of 8 and 10, we see that the additional two words are used to include more information, such as the day of the meeting in Example~2 or the gender of the injured person in Example~3.

\input{tables/examples.tex}
\section{Conclusion}
We proposed a novel word-extraction model for sentence summarization that generates summaries by optimizing an objective function of language fluency and semantic similarity. A hard length constraint is also imposed in our objective function. In a controlled experiment, our model achieves better performance than strong baselines on headline generation and DUC2004 datasets.

\section*{Acknowledgments}
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), under grant Nos.~RGPIN-2019-04897, and RGPIN-2020-04465.
Lili Mou is also supported by AltaML, the Amii
Fellow Program, and the Canadian CIFAR AI Chair Program. This research was enabled in part by the support of Compute Canada (\url{www.computecanada.ca}).

redpajama_set_name: RedPajamaArXiv


Pick up your science notebook and a glue stick.
On page 18 and 19, paste in page 3 and 4 of our new Seasons notes!
Take a "Check What You Know" sheet from the front counter and answer #1.
What connection can we make between changing hours of daylight and seasons?


After the events in Charlottesville, Virginia on Aug. 12, officials at University of Texas at Austin cancelled the torchlight parade on their campus. After both of these events, MSU officials debated whether to keep the MSU homecoming tradition.
A survey went out to students, administration and organizations asking if the torchlight parade should still continue.
A task force was put together for the purpose of finding information and to come up with a solution to continue it or not.
Task force members were charged with three goals: find out why we do the bonfire, determine if the university should continue it and research the history of torchlight parades.
"We all did research to make sure it was something that we should continue," Ramirez said.
Twitter poll posted on Oct. 13.
The response rate was high as far as how many people said yes we should continue it.
Because the torchlight parade is an event that could possibly injure someone, there will be extra staffing, however, they are not changing security because of the event in Charlottesville.
Housing, Resident Assistant's, MSU police, fire department and staff will be helping throughout the entire parade and throughout the bonfire to ensure student's safety.


text:
Parents' fear post-Covid mental health epidemic
Three in four parents say they are worried about their child's mental health following the pandemic, a new survey has found.
The survey of more than 2,000 parents, commissioned by BBC Bitesize and Netmums, found that 74% of parents are concerned about their child's mental health, with the same proportion reporting that their child's mental health had become a greater priority for them since the pandemic.
Nearly half, 44%, said that their child had experienced mental health challenges, and 46% said their child had been lonely because of the pandemic.
The results also showed the impact of lockdowns on children's social skills and confidence.
In total, 48% reported that their child was having difficulties with friendships and socialising, and two in three – 66% – said that their child had been anxious about not being able to see friends and family as often as they usually would.
Nearly three in 10 – 28% – said that their child's mental health had deteriorated over the last six months.
BBC Bitesize's Parents Toolkit and Netmums have launched a collaboration, #LetsTalkAboutChildMentalHealth, connecting parents with mental health experts, as well as providing information to help them talk to their child about mental health.
A Mental Health First Aid Kit will be available for families needing to access help, and educational psychologist Laverne Antrobus will give advice to parents on how to discuss the issue with their children.
The charity YoungMinds will also provide tips on how to encourage children to engage with life outside the home post-pandemic.
Helen Foulkes, head of content production from BBC Bitesize, said: "The statistics speak for themselves, concerns about child mental health and the overall wellbeing of families post-pandemic are reaching a crunch point."
Netmums editorial director Anne-Marie O'Leary said: "Parents have told us since the earliest days of the pandemic that their children's mental health was becoming a growing concern."
She added that the latest research "evidences just how severe the cumulative impact of two years of interrupted schooling and social distancing measures has been".

redpajama_set_name: RedPajamaCommonCrawl


text:
Donal McLaughlin
by Donal McLaughlin
Paisley, Scotland, early 1970s
They were living at the top of the Steps at the time, where the street veered away to the right, round to the shops wi the CO-OP at one end, GALBRAITHS at the other, then came a patch of grass, Liam minds, L-shaped, the wrong way round but, on a slope leading down to a drop, the four- or five-foot drop to the car park that wasn't a car park, to the loading bay sort-of, in behind the community centre. Grass maybe, they never ever played football there but, no buckin wonder if ye think about it. Next anyway, at street level, came the library, at right-angles to the community centre under it, & the first time he went in – to the library itself lik – he got into trouble twice. First, he was caught at the ADULT shelves, the first books ye came to, on your right as ye entered through the glass. Youngfella, God love him, meant no wrong, was realising himself probably just when up popped a librarian to chase him, a librarian wi hair round her wrist, a band of hair round her wrist, dark hair too, lik a bracelet lik. He shouldn't be looking at them-there, says she, pointing at the books, they weren't suitable sure, for youngsters anyhow & before he knew it, she was showing him the children's bit, had led him over to the children's bit, to where the Bobby Brewsters were & Enid Blytons. The Secret Sevens were maybe too young for him maybe, she said, the Famous Fives too old, The Mystery of's were definitely too old, she warned, touching the top of one. Naw : start wi the Famous Five maybe & see how ye get on. An' at that, she left him to it, left him to his own devices, which is how the weeboy then got into more trouble: didn't he go 'n' pick too many, more than you're allowed anyhow, not lik that it was even his fault lik, how the hell was he supposed to know, sure she hadn't mentioned a limit. Not even adults were allowed to borrow that many, says she but, as if that made it even worse & anyhow: he'd have to apply to join first if he hadn't a ticket, his mother or father would have to fill a form in, it didn't cost nothing but & was easy to complete. His daddy – he toul her – was still at work & the library would be closed before he got home, that was alright but, she said: it didn't have to be the man of the house, his mammy could do it an' all, so he ran back round, Liam, & was back in the library in no time, which must've seemed suspicious right enough, even more so than his accent – Irish still, at that point, Northern Irish into the bargain – which, if truth be told, is what had got her suspicions up first-day probably, had made her suspicious in the first place. Seeing the address but, how near-hand it was, she'll have given him the benefit of the doubt maybe, decided his mother did fill it in & so aye: off home he got to go, all proud lik, wi his first-ever library books, first two library books & as his mother will tell ye, he was back for the place opening the next morning, morning after that an' all & the day after that & day after that, would've been back on the Sunday an' all, whoever seen him, if the place hadn't been shut. Devouring the books he was. So much so, they even started quizzing him to check, check was he actually reading them, properly lik. There was no catching him out but but, & so aye: things continued lik that, he turned up every other day at least & eventually they eased off on him, took to him maybe even, the lady librarians anyway & he couldn't believe his ears so he couldn't when one day her wi the hair round her wrist toul him – in front of the other two too – to get his mum to apply to join, cos if she – as an adult – applied, she could borrow more books, up to four , the odd one for him an' all in other words, wasn't as if she was restricted to adult books, was it. Happy days! Having asked their ages, they even said Ciara & Annette & Bernie & Sean could apply an' all, Cahal & Orla were still too wee, could apply too but of course in due course.
He loved the library, the weeboy, couldn't get enough of it. He went round to it so much, became such a familiar face, it went from that to him getting, one time, to stamp one of his books himself, to him getting to stamp all his books himself, to him getting to stamp the ones his sisters were borrowing an' all. Was no time of course before the girls wanted to too, to stamp their books too & sometimes, if the place was dead lik, they got to. It wasn't the same but, didn't feel right, felt awkward in fact. The librarians hadn't to say nothing, he worked it out for himself: they were overdoing it, overdoing it so they were now, was better to go on his own, there was more chance of getting to help lik if the rest stayed back in the house. That's when he started slipping out the back, nipping round on his own just, making sure none of them follied him & soon it wasn't just Louise, the one wi the wrist hair, who let him; naw, soon Alison, the blond one, was letting him an' all, Heather – her wi the thick-thick glasses – too. Aye, them-three were sound, dead-on lik. He knew but not to ask when the boss was there, him wi "Head Librarian" on his badge, only fella into the bargain. He was a bit wary of him . Louise, Alison & Heather on the other hand did nothing but encourage him.
Youngfella went from stamping books to helping to put them back. First time he asked could he, Alison (it was) hesitated maybe, let him do one but, to see how he got on lik. She let him do more another day & another day after that, always double-checking of course, always making sure he got it right. Turned out: she could rely on him, a dab hand at alphabetical order he was, even when it wasn't just the first letter but the third or fourth or fifth that counted. Soon he was asking at the desk was the trolley full again yet & if so, could he put them back? They let him, aye & soon the trolley couldn't fill up fast enough. He'd be standing there waiting, hovering over it, willing more & more people to bring back their books. Especially, lik, the ones who lived across the street. Uri Geller had been on the telly recently, so he stood there, Liam: staring across; willing & willing the neighbours to bring their books back.
Then something else happened. Before he knew it, they were letting him in behind the counter an' all, he was allowed in beside them for a chat. By this stage, he was stamping everyone's books: all the books of all the users. To be on the safe side, Alison or Louise or Heather would stand nearby & watch. The borrowers seemed not to mind, would just smile just. 'Ye'd better watch or he'll be doing ye out of a job,' the older ones would joke. 'Ye'll end up on the broo.'
Not that the weeboy was paid lik, for all the work he was doing. There was no way they could've paid him, short of out of their own pockets sure. He wasn't looking for payment but.
What did happen one day – boss was off, needless to say – was: they took him up the back of the library, into where it said STAFF ONLY, in behind where Children's Storytelling happened sometimes & showed him a pile of books that were about to be WITHDRAWN. Liam hadn't a clue what they meant. His Granda Cluskey had been 'withdrawn' sure, wile wile withdrawn, when Mammy Cluskey died. How could books be lik that?
The librarians had to explain. 'And don't worry,' they said. 'Ye don't have to pay nothing. We're giving ye first pick as a reward for all your help. We're about to put them out for people to take for free. You've first choice but.'
WITHDRAWN was already stamped on them, they showed him.
That was hit. He didn't need a gold invitation & they let him take every single one he picked. As many as he could carry & more.
Heather, or was it Alison, turned up at the door wi him, to assure his poor mother he wasn't robbing them. Some sight it must've been right enough: a weeboy of nine-still carting not just storybooks but a three-volume dictionary along the street.
Dictionary was something else so it was. His da let him keep it in the bookcase. A-G, H-R, S-Z. Was all the youngfella could do to lift the bloody things.
As time went on, of course, the dictionaries helped wi his homework. Sometimes a drawing or photo did the trick. Sometimes the meaning of the word. They were a great help to his sisters & brothers an' all when their time came. An' once he himself was that bit older got, fifteen, sixteen, say, they helped him wi the crossword in the Record . Any time he got stuck, the clue was too cryptic lik, he'd end up flat on the floor, face-down but up on his elbows, Volume III, say, open in front of him & he'd leaf his way through the U's, say, page after page of bloody U's, till – phew! – he came upon the six-letter U that made sense as well as fitted, that was the word he needed.
Phew right enough: some buckin slagging he'd've got otherwise sure when his da got back from work. – For starting something lik he couldn't finish.
Donal McLaughlin writes short stories and translates novels. He is the author of two collections, an allergic reaction to national anthems & other stories (Argyll 2009), and beheading the virgin mary, and other stories (Dalkey Archive 2014), which include his "loose sequence" of 'Liam stories' that move between Scotland and Ireland. As a translator, Donal was shortlisted for the Best Translated Book Award (USA) in 2013, and awarded the Max Geilinger Prize (Switzerland) in 2015. He maintains a web presence at www.donalmclaughlin.wordpress.com


Yannick Eckmann stands out in a crowd. Every so often an athlete comes along who appears to have the right stuff. It is apparent when you watch their body in motion that they are not like the others. Those who work equally as hard, strive to improve, yearn to achieve, want to be the one who has got it all, but are not. No matter what else happens in their life outside their sport, there are certain athletes with a recognizable gift who are destined to be great in competition.
Yes, that is right Yannick is only 18 years old (November 30, 1993). His parents were there yesterday to cheer their son in full support; thank you for buying him his first bike! I sometimes think it a miracle when any young person finds their gift so early in life. I hope his path into the future is a good one, full of individuals able to help develop his talent, and void of barriers that too often threaten the careers of star athletes. "He looks like a natural up there," I commented to his mother as he stood on the top step of the podium yesterday, "yes" she smiled sweetly in recognition of her son's talent.
Currently riding for the Pearl Izumi/Shimano cyclocross team, Yannick was born in Germany where he began mountain biking at a very young age with his older brother Robin, an accomplished cyclist himself. The family moved to the United States in 2005. Yannick is a full-time student at Boulder High School, yet manages to travel around the country and Europe to compete in races. He also races road, but as his mother commented in delight yesterday, "he likes the mud." It is good that someone has found a use for mud.
In his first year after competing as a Junior, Yannick Eckmann ended up second in the U23 (under 23) 2011 Exergy Cyclocross US Grand Prix (results) behind Zach McDonald (Rapha/Focus), who is 2 years his senior. Cody Kaiser (Calgiant/Specialized), another promising U23 rider, came in third. We will be seeing much more of these three athletes in the years ahead. Move over Ryan Trebon, Jeremy Powers, Tim Johnson, Geoff Kabush, and Jamey Driscoll, the next wave of CX riders is closing in.
Yannick Eckmann reminds me of a freestyle mogul skier on the bumps - eyes far ahead, down the hill, his body instinctively knows where it is at, his brain has already processed what is below him, his focus is meters up the road at what is coming next. Makes for a great cyclocross racer, and a great talent for us to watch into the future.


ROBBINSVILLE, N.J. (WABC) -- Residents in one New Jersey town say the holiday rush at a local Amazon warehouse is leading to a traffic slowdown on their roads.
Robbinsville Mayor Dave Fried threatened legal action Wednesday after Amazon sent a public affairs staffer to a meeting to discuss solutions to the traffic that's clogged area roads.
Fried says the meeting was supposed to include operations officials.
NJ.com reports traffic grinds to a halt for miles when the fulfillment center's more than 4,000 employees are going in and out of the facility during rush hour.
Fried says the infrastructure wasn't built to handle this much car volume. He hopes Amazon can change or stagger shifts.
Fried said the traffic problem is affecting several towns in the area.
He said the fact that no operations officials attended the meeting was a sign that Amazon is not taking the problem seriously.
"Now featuring the busiest warehouse on the planet, Amazon has dramatically increased the number of workers at the Robbinsville center during its peak hours as it gears up for the holiday rush. Unfortunately, that is double the number of employee "trips" they were approved for by our Planning Board in 2012. One possible remedy we have continuously suggested is having Amazon stagger the shifts of its workers in and out of the complex during those peak hours. Those suggestions have been ignored."
Fried went on to say the congestion is causing unprecedented gridlock on local roads, with children unable to get to school and residents unable to pull out of their driveways.
He said police data shows there have been 25 accidents attributed to workers coming to and from the warehouse over the past six weeks.


In 2018, it was reported that Spotify listed Emo Rap as it's platform's fastest growing genre of the year. You can view that report HERE. For those unfamiliar, Emo Rap is a fusion of modern hip-hop with, you guessed it, emo music of its various sorts. As weird as that may sound, you already know some of the household names listed as part of the genre. Artists on the big stage include Post Malone, Juice WRLD (whose latest offering topped the Billboard 200), Lil Uzi Vert, and rising star Lil Xan. Other artists known for the genre who are quickly on their way to hitting that mark include Scarlxrd, Wicca Phase Springs Eternal, and Nothing, Nowhere. To wrap up the mentions, the late Lil Peep and XXXTentacion were also pathways into the genre.
This brings us to a guy who has made his name known and life dedicated to rising young bands named Greg Long. Long has for the last nine years run We Are Triumphant who has been home to bands such as Neck Deep, Oh, Weatherly, Traitors, among others to help kickstart their careers. The passionate man he is about helping newer artists, Long has been planning GRXTESQUE, a new alternative rap label for upcoming artists in the genre.
GRXTESQUE is a new label launching on the 29th with our first artist ✦pink cig✦. A young artist from Phoenix, AZ who has been able to mass over 150,000 monthly listeners on Spotify alone monthly. With tracks like Xanny Bars 222 and Angeldust sitting in the millions. He has mainly been discovered prior via Soundcloud and being on Spotify's official playlist "Tear Drop" right next to the likes of Lil Peep, XXXTENTACION, Trippie Redd and more. The song "Loser 00" takes a different approach to the traditional theme of materialism in hip hop. This track is the first single off Pink Cig's debut album Beautiful Strangers due out this summer.
GRXTESQUE also has distribution via Symphonic Distrobution which services some of the bigger names in hip-hop such as Waka Flaka Flame and Wiz Khalifa along with EDM superstar Marshmello.
Another artist with Pink Cig on GRXTESQUE isKEEPMYSECRETS who we have highlighted on this site before as well. We can expect his debut on April 12th along with a collab with Pink Cig in the future as well.


text:
Listen to "I Can Do It" by thisisNAMASTE
Since the summer, emerging pop newcomer thisisNAMASTE began rolling out a succession of introductory tracks. To date, she has given us two fine, pristine offerings in "Exactly What You Want" and "Not My Fault" yet, Miss unstoppable is keen to follow up with even more charismatic tuneage. With "I Can Do It" the up-and-coming singer turns on the gas, full-on embracing some vibey, electro-pop stylings. She sings, cool and confidently as ever. Making her biggest impression so far by being sweet, but direct, empowering and in charge.
thisisNAMASTE explains…
"I wrote "I Can Do It," as I got fed up of many situations I found myself in, in which some people questioned a girls ability to do certain things that nobody would question a guy doing. Whether it was lifting something heavy to doing an unconventional job. I wanted to write a tongue-in-cheek song that was fun and empowering for young women."
There is no doubt "I Can Do It" is both powerful and endearing. A carefully crafted pop song which relates to women around the globe. It also garners attention because of the tracks dance-pop grooviness, and because the melody is as catchy as, a dose of the common cold. Needless to say, it is much more pleasant than actually having the symptoms of any of the winter illnesses currently doing the rounds. Although I know for certain, I don't have a need to ring up a doctor for a consultation about how 'sick' the track is. As, after a handful of spins, I came to this diagnosis about "I Can Do It", all by myself.
With three brilliant, songs under her belt, all of which validate thisisNAMASTE's capabilities and potential. There is no doubt that we are going to be hearing a lot more from this pop star, going forward.
Connect with thisisNAMASTE
Facebook: https://www.facebook.com/thisisnamaste
Twitter: @thisisNAMASTE
Instagram: https://www.instagram.com/thisisnamaste/
Listen to "Not My Fault" by thisisNAMASTE
Listen to "Girl I Got You" by thisisNAMASTE
Mandy Rogers July 7, 2020


How Should Male Dogs Maintain Love Relationship?
What Kind of Women Do Dog Men Like?
When romantically involved, men under the Dog sign would generate a desire to conquer and control their hearts strictly. If they really love someone, they would become quite down-to-earth, gentle and careful. The upright, honest and understanding male Dogs are very constant in love and they look forward to the compatible family relationships. Dull as they are, male Dogs may not express with sweet words but they are quite sincere and wholehearted and would show their sincerity from dribs and drabs of life.
Men born in Dog years actually have many unique merits - they are passionate, decisive and like to challenge and conquer. The more they love someone, the more inclusive they will be. Sometimes, they can be selfish. For example, they are determined to win every quarrel and like to put on airs and pretend to be a grown man; many of their childish behaviors make girls insecure. If men of Dog sign can be more considerate, steady, gentle and meticulous, their girlfriends will award them more marks and many contradictions will be avoided. In this way, they can enjoy a better relationship and stay together for a lifetime.
Seemingly amiable, Dog men are actually sensitive inside and they hardly reveal their inner thoughts to others, even the closest ones. When they are asked about the past, they would return a vague answer. If you find your boyfriend under the Dog sign likes to talk about his past and childhood stories, it means he is in you and wants you to know more about him and he has dropped his guard completely to you. Also, he would put you first and give up his hobbies to company you. If he can do that for you, he must love you very much.
Candid and straightforward in daily life, male Dogs are traditional and attach great importance to family. While choosing partners, they prefer the family-oriented women who can take care of every family member and help them to work without the worries behind. As a result, they generally show no resistance to the gentle and virtuous women.
Male Dogs handle things in a mature, practical and quick manner and are easily attracted by women who also work positively with excellent execution but dislike those who merely talk and take no action.
The just and equitable Dog men don't like dirty tricks and love the kind women in daily life. Also, they are of high self-esteem and wish their partners could respect them and think the way they do. Because of this, they often fall for the kind and considerate women.


It helps to understand how the Illinois state court system works when you're trying to find court records. The Illinois trial court system consists of Circuit Courts , …... Illinois Criminal Records are now available online. You can search Illinois Criminal Records by using one of the links on this page. If you want to find criminal record information about someone from Illinois, you have come to the right place.
US District and Circuit Court Records (Chicago) Records of the U.S. Circuit and District Courts, Northern District of Illinois, Eastern Division, Chicago These records are held in the National Archives regional facility in Chicago, Illinois.
How To Find Criminal Record santa clara county divorce forms sacramento county public records search background checks in california Therefore, if you consider the size of California is, you can imagine how difficult it would be to get these files on a district level.


Social media, m-commerce, e-commerce and globalisation have changed the way consumers interact and spend, adding new dimensions to the retail environment. This increasingly vast and complex environment brings great opportunities, but also great challenges for fashion line planners and merchandisers.
Today's fashion retailers face constant pressure to attract and retain customers who are empowered, informed and connected by sophisticated digital technologies. They need to catch consumers' attention with on-trend assortments while delivering a consistent and memorable brand experience across channels, locations and cultures – all while meeting or exceeding margin targets. In this highly-connected world, digital line planning and merchandising technologies are transforming the way fashion brands and retailers create and deploy collections.
Line planners and merchandisers are responsible for decisions that directly affect the balance sheet. As the gatekeepers of the consumer experience, the products and assortments they choose and the way they present them are pivotal to success. But trends, and competition, move fast in this industry and new lines are coming to market faster than ever before. Those decisions need to be informed by a full, up-to-date picture of what's going on, and digital merchandising software provides that, bringing all that information together in real time to deliver a 360-degree view of the latest collections, trends, customer demands and sales figures. It enables planners and merchandisers to capture retail performance in real time, so they can see clearly what works and what can be improved and can make informed decisions based on the trends that are driving sales right now.
Visual merchandising is both an art and a science. It can take many iterations to showcase the products in a way that will entice the consumer and give them a unique and memorable brand experience. As such, merchandisers need to experiment with a variety of aspects including shop-floor location, fixtures and lighting effects as well as the garments themselves. Physically trying out each idea to see how well it works is time consuming, and this presents a growing challenge for merchandisers who are under pressure to introduce more new lines at increasing speed. If you can remove the need for physical tests and visualize assortment iterations in 3D, imagine how much space that would create to experiment with new merchandising ideas.
Today's visual merchandising software is equipped with the latest 3D visualization capabilities so that merchandisers can build their vision using 3D images of real products. They can try out innovative ideas and, if something doesn't quite work, make changes quickly before implementing their ideas in the store. By enabling merchandisers to create endless iterations quickly at low cost, the software creates space to try out innovative ideas that can really differentiate the brand.
Once the vision has been perfected, it can be shared across different stores complete with 3D walkthroughs that bring the ideas to life. Line planners and merchandisers can work together and get early-stage feedback on proposed collections and store set-up. They are empowered to provide assortments that suit the characters of different cities and locations while delivering a consistent brand experience across different locations.
Real-time information and 3D visualization delivers huge benefits for merchandisers, enabling a creative, innovation-based workflow that is much greater than the sum of its parts. By enabling collaboration on assortment planning and creating space to try out innovative ideas, visual merchandising software empowers merchandisers to build entertainment and interaction into every shopping experience so consumers feel truly connected to the brand. Merchandisers who can gain a rich, real-time understanding of consumers' expectations can create an engaging story that will enhance the consumer's relationship with the brand as well as generate sales – and those are impacts that every business wants to see.
Want to know more? Watch this Apparel Magazine webinar on 3D Merchandising!
Previous PostWhy 3D technologies can help stores improve consumer experiences?


text:
THEATRE GUIDELINES
Februrary 1- 8:00 PM
Join us for Prom Night backstage with a live band,costume contest, and more. We'll provide the punch!
18+ show
If it was popular in the 90s, the band Flannel does it. All the hits from all the genres that made the 90s great. Nirvana, Alanis Morrisette, Montell Williams. All that stuff.
Fabulous Armadillos: What's Goin On
March 28th at 7:00 PM
"What's Going On" is a moving concert experience that incorporates live music, live narration and video to provide the audience a multi-media deep dive into the 60's and early 70's and of course, the Vietnam War. Through song and imagery this concert examines the tumultuous time period but concludes on a note of hope and inspiration for a better tomorrow. As one Vietnam veteran narrates during the show, "When we think of our divisions today, they pale in comparison to how badly we were divided back then". A must-see Fabulous Armadillos production.
Alma Andina
April 4th at 7:00 PM
Alma Andina, or Soul of the Andes, is a Minneapolis-based group that performs Andean and Latin American music. The group's director Vladimir Garrido hails from northern Chile, where his mother Nia has been singing folk music for over 30 years. He created Alma Andina in 2007 with his cousin and uncle, and when his uncle returned to Chile, Vladimir sought new band members and recruited Nicolas Munoz and Matthew Levin, young talented Chileans.
The band was joined by two Americans, both of whom had lived in Chile and learned with its music and culture: Emily Savage, charango and flute, and guitarist Matt Kitchin. The group recorded an album that has helped bring the music they perform live to a wider audience. They are currently working on their second studio recording. Alma Andina derives its inspiration from a love of folklore, and a desire to share and promote Latin American music and the rich cultural heritage of the Andes.
April 25th at 2:00 PM
Get your singing voices ready, Minnesota, VocalEssence is coming to you! Weaving a traditional concert and a community sing into one, this performance is going to make you tap your toes, clap your hands, and sing your heart out. Including favorites from the stage and screen, gospel, folk, classical, and pop music, there is a little something for everyone to love. Bring your outdoor voices!
Founded in 1969, Minneapolis-based VocalEssence has decades of history as one of the world's premier choral music organizations. But despite its global influence, VocalEssence has kept its focus local—consistently pioneering ways to strengthen Minnesota's community through thrilling musical experiences.
thememorialauditorium@gmail.com
MEMORIAL AUDITORIUM PERFORMING ARTS CENTER
714 13TH Street - Worthington, MN 56187


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Football Betting Tips – Arsenal vs Man United
'Best Bets' is designed to be a really easy and enjoyable service to follow along with just a couple of emails a week and a handful of well researched and supported bets recommended to you each week. If this sounds like something you'd enjoy why not join us for the full European season for just £9.95 a month or £77 £37 for the whole season.
Premier League Tips
Arsenal vs Man United
– Over 2.5 goals in the last four fixtures; over 3.5 in last three of four
– Man United are unbeaten in last four vs. Arsenal
– Both have won four of their last six in all comps, losing one and drawing one
– One point separates both sides, with just two goals difference
– Arsenal have won their last eight home matches in the Premier League; dropping seven points at the Emirates all season
– Man United have won their last nine away matches in all comps
"Ole's at the wheel. Tell me how good does it feel". It must feel bloody great. United's Norwegian heart-throb has transformed this United side into winners again. Wednesday night's win in Paris was not in the script, even by United fans' wildest dreams. Priced at 19.00 to qualify for the quarters, a last-minute VAR-inspired Rashford penalty saw the Red Devils complete "mission impossible" – recording their ninth consecutive away victory.
Arsenal will have been watching on with mixed feelings. Sure, United will be pumped coming into this Sunday's clash, but with notable names still expecting to be missing for the trip to the Emirates, how much gas is left in Ole's tank?! The inclusion of teenagers Tahith Chong and Mason Greenwood late on was a joy to see, but a hand that was forced. Inexperience may be exposed in a Premier League fixture when more is expected.
Arsenal's home form in the Premier League has been impeccable. A solitary loss to Man City on the opening weekend of the season and two draws to Liverpool and Wolves have been the only issues on home soil this campaign. Emery has tapped into the importance of home form, and with two or more goals scored in their last six Emirates fixtures, the Gunners are brimming with confidence in North London.
One small caveat. Man United's 3-1 FA Cup win at the Emirates at the end of January will play heavy on the host's minds. United's pace and counter-attacking philosophy was the reason behind this win; with Martial and Lingard still major doubts, Solskjaer will have to implement a Lukaku-based offence to break down Arsenal.
The Belgian striker is finding his form again. Two goals in Paris followed on from a brace in his previous two matches against Southampton and Palace. He'll always be a threat in this reinvigorated United side, with Arsenal's lack of pace at the back under scrutiny this weekend.
It's a case of home form vs. away form, on Sunday. Which will break first? With just a point separating the sides in the league, a win for either will put down a big marker in their respective quests for a place in the top four. Ole's injuries may finally catch up with him this weekend, with a draw not a disaster for either.


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Q: use data when a resource exists, otherwise create a new one terraform has resource and data. resource creates a new thing, while data refers to an existing thing.
Is it possible to do the following?
1) if a resource does not exit, create a new one by 'resource'
2) if it exists, refer to it by 'data'
For example,
# if not exists
   resource "a" "b" { name = ...}
# else
   data "a" "b" {name = ...}

resource "c" "d" {
   field = "${element(concat(a.b.*.name, data.a.b.*.name), 0)}"
}

A: This blog post describes a way to accomplish what you're trying to do by using a variable of type object with the minimum required (shared) attributes and passing that in.

redpajama_set_name: RedPajamaStackExchange


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Q: Is there any alternative to stdout.line in ansible? I am facing an issue with stdout.lines in ansible. Are there any alternatives available?
This is my code
- hosts: myhost
  tasks:
    - name: get contents
      shell: "ls /home/*.iso | cut -d '/' -f 7 "
      register: prod

    - name: Copy
      community.vmware.vsphere_copy:
        hostname: host
        username: myuser
        password: mypass
        src: "{{ my_path }}"
        datacenter: VMFStorage
        datastore: SAN-0
        path: "/test_path/{{ prod.stdout_lines }}"
        validate_certs: false

When I run this, the output I'm getting in UI is
['test.iso']

But the expected output is
test.iso

Any help would be appreciated.
[EDIT]
Tried using the find command
- hosts: myhost
  tasks:
    - name: find the file
      find:
        paths: /home/
        patterns: '*.iso'
      register: prod
    - debug: var=item.path
      with_items: "{{ prod.files }}"

    - name: Copy
      community.vmware.vsphere_copy:
        hostname: host
        username: myuser
        password: mypass
        src: "{{ my_path }}"
        datacenter: VMFStorage
        datastore: SAN-0
        path: "/test_path/{{ prod.files }}"
        validate_certs: false

Also, in the "path" defined under community.vmware.vsphere_copy module, I just require the name test.iso instead the whole path /home/test.iso
Output of debug:
 [myhost] => (item={'path': '/home/linux.iso', 'mode': '0644', 'isdir': False, 'ischr': False, 'isblk': False, 'isreg': True, 'isfifo': False, 'islnk': False, 'issock': False, 'uid': 0, 'gid': 0, 'size': 1295247360, 'inode': 134527790, 'dev': 64776, 'nlink': 1, 'atime': 1648020499.1899607, 'mtime': 1648013066.2809734, 'ctime': 1648020450.6790545, 'gr_name': 'root', 'pw_name': 'root', 'wusr': True, 'rusr': True, 'xusr': False, 'wgrp': False, 'rgrp': True, 'xgrp': False, 'woth': False, 'roth': True, 'xoth': False, 'isuid': False, 'isgid': False}) => {
    "ansible_loop_var": "item",
    "item": {
        "atime": 1648020499.1899607,
        "ctime": 1648020450.6790545,
        "dev": 64776,
        "gid": 0,
        "gr_name": "root",
        "inode": 134527790,
        "isblk": false,
        "ischr": false,
        "isdir": false,
        "isfifo": false,
        "isgid": false,
        "islnk": false,
        "isreg": true,
        "issock": false,
        "isuid": false,
        "mode": "0644",
        "mtime": 1648013066.2809734,
        "nlink": 1,
        "path": "/home/linux.iso",
        "pw_name": "root",
        "rgrp": true,
        "roth": true,
        "rusr": true,
        "size": 1295247360,
        "uid": 0,
        "wgrp": false,
        "woth": false,
        "wusr": true,
        "xgrp": false,
        "xoth": false,
        "xusr": false
    },
    "item.path": "/home/linux.iso"
}

A: loop over prod.files, take the path key and use the basename filter:
- name: Copy
  community.vmware.vsphere_copy:
    hostname: host
    username: myuser
    password: mypass
    src: "{{ my_path }}"
    datacenter: VMFStorage
    datastore: SAN-0
    path: "/test_path/{{ item.path | basename }}"
    validate_certs: false
  loop: "{{ prod.files }}"

see filters


Workplace Safety Blog | Safetycare | Safety Training Videos | Free Safety Pictures and Resources: Need more height???
Over the years, we have published many examples of work on roofs without perimeter protection, misused elevating work platforms and misused forklift trucks. But when you get all three happening in the one photograph, you know you've hit the jackpot!
It hardly seems possible that anyone could come up with a more dangerous or more ridiculous way of doing roof work than this Absolute Shocker from Indiana, USA.


text:
Home / Past Forum Meeting Minutes / May 4, 2011
Employee Forum Agenda
NOTICE: The Forum's annual portrait will take place on the steps of Wilson Library at 9:05 a.m. before the meeting. Please don't be late for the photograph!
9:15 a.m. —-Meeting: Pleasants Family Assembly Room, Wilson Library
II. Welcome Guests & Members of the Press
III. Special Presentations
Swearing in of New Delegates
IV. Human Resources Update—Vice Chancellor Brenda Malone
V. Approval of April Minutes
VI. Old Business
Resolution 11-01 Concerning the Promotion of Fair and Equitable Human Resources Policies at the University of North Carolina at Chapel Hill (Tabled)
Resolution 11-02 Concerning Sexual Harassment and Sexual Assault (Second Reading)
Personnel Flexibility Proposal (Senate Bill 575)
Tom Ross, President of UNC General Administration
William Fleming, Vice President for Human Resources of UNC General Administration
Ann Lemmon, Associate Vice President for Human Resources of UNC General Administration
Tom Shanahan, Associate Vice President for Legal Affairs of UNC General Administration
VII. New Business
Officer Candidate Speeches
VIII. Forum Committee Reports
Carolina Community Garden Advisory: Joshua Boone (April 6 minutes)
Communications and Public Relations: Carrie Aldrich
Compensation & Benefits: Dan Barmmer
Education and Career Development:
Legislative Action: Danny Nguyen
Membership & Assignments: Myra Quick
Recognition & Awards: Chris Meinecke
Staff Relations, Policies & Practices: James Holman ( March 30 minutes)
UNC System Staff Assembly: Chuck Brink
Executive Committee: Jackie Overton
IX. Announcements/Questions
X. Adjournment
Employee Forum May 4, 2011 minutes
The Chair called the meeting to order at 9:15 a.m. She welcomed members of the press, and new and old delegates. She said that she had rescheduled Carolina Counts presentations from Carolyn Elfland in order to make time to discuss the personnel flexibility issue. The Chair led a rendition of the Forum Charge, and then conducted a round of introductions for incoming and outgoing delegates.
The Chair noted that this was Employee Appreciation Week. She noted that the new delegates had attended the May 2 orientation. She introduced Senior Director of Human Resources Gena Carter to present the Forum's customary Human Resources update. Carter noted that annual reviews must be compiled by May 31 and be inputted into the Human Resources Information System (HRIS) between May 16 and June 24. Interim Benefits Counseling Director Ashley Nicklis said that health insurance enrollment will take place between May 9 and June 8. As of that day, there would be no premium increases and employees would have to attest to their body mass index (BMI) and tobacco use. All employees will be placed in the 70/30 plan unless they attest to BMI of less than 40 and no tobacco use or participation in a tobacco cessation program. The State Health plan will now cover dependents up to age 26 and will remove preexisting condition disqualifications for those under age 19. She said that the University might have to undergo a second enrollment if the Legislature changes the fee structure for the State Health Plan. Chuck Brink asked if there were an approved BMI chart. Nicklis said that the enrolling website will contain a BMI calculator. Alan Moran asked if Human Resources would undertake an enrollment campaign to help employees without computer access. Nicklis said that Human Resources had pulled around 600 employees and is attempting to find computer space to enroll these employees. Moran asked if there were a contingency plan in place if employees do not receive the information. Nicklis said that employees must fill out the information themselves as the attestation represents a legally binding document. Jonathan Stephenson asked about opting out of the State Health Plan. David Schwarz confirmed that employees would not receive money back if they chose to not enroll in the plan. Stephenson said that his premiums under the State Plan had become unaffordable so he had chosen to enroll in his wife's plan at Duke University.
The Chair called for a motion to approve the minutes of the April meeting. Dan Barmmer made this motion seconded by Chuck Brink. The motion was approved by acclamation.
Carleta Long read resolution 11-01 concerning promotion of fair and equitable Human Resources policies at UNC-Chapel Hill. The Chair noted that this resolution was under consideration on second reading. Dan Barmmer suggested that the resolution remove the requirement that the Chancellor approve all policies. Charles Streeter said that departments have the right to make policies without the approval of the Chancellor or Human Resources. Barmmer said that the definition of policy in this instance meant those areas which could have a negative impact on pay or benefits. Associate Vice Chancellor for Human Resources Matt Brody said that University-wide policies are approved by the Vice Chancellor for Human Resources or the Chancellor. He said that it is not viable to require such high level approval for work rules and standard operating procedures which number in the hundreds. Jessica O'Hara disagreed with the direct reference to Facilities Services in the resolution. Marc ter Horst said that only severe policies resulting in a loss of pay or benefits should receive high-level approval. He thought it alright to remove the Chancellor from the list in the resolution. Carter noted the distinction between policies that are administered differently between or within departments and policies that are written differently. Aaron Williams asked if workplace violence should fall within the resolution. O'Hara moved that the Forum send the resolution back to committee to rewrite the last paragraph distinguishing between policies and standard operating procedures. James Holman said that he would send the resolution to the Forum listserv to get edits and thoughts from members. The motion was approved by acclamation.
In the interest of time, the Chair asked that the Forum take up resolution 11-02 at the June 1 meeting. The Forum agreed to this request by acclamation.
The Chair introduced UNC System President Tom Ross, noting that Ross had taken office on January 1. Ross noted his background as a Superior Court judge and Davidson College president. Ross said that he tried to manage his departments in an inclusive way. He invited employees to check out his record as a manager. Ross had received questions as to why the University System had sought personnel flexibility this year. He said that his office had consulted with University leaders to find policies that would be most helpful to campus operations. He had received a lengthy list that included construction, planning, real estate, and human resources initiatives. He said that the University was pursuing the opportunity to design a new personnel system to do more for the University with no intent to change the rights and status of University employees. He said that some misinformation had circulated about the initiative. Ross noted the move of Health Care System (HCS) employees out from under the State Personnel Commission that had led to the use of non-State dollars to improve staff salaries. He noted that the community college system was not part of State Personnel Act and had retained protections. He proposed a hypothetical situation in which the University chose to close down between Christmas and New Year's, excepting laboratory work and paying staff. He said that the University could not do this under the current system. Ross noted that University staff have not received a pay increase for 3-4 years. Ross said that the Board of Governors is more likely to feel love and care for University employees than is the General Assembly. The Board knows that the University will not run well without its employees. He did not know that a sense of mutual interest extends beyond the University.
Ross noted questions as to why the University General Administration had not authored a detailed plan concerning personnel flexibility for general consumption. He said that General Administration did not want to give the Legislature a plan it could dismantle piecemeal. He said that it was not true that the plan would make employees at-will. Ross offered the assistance of his staff to answer questions from the Forum. Marc ter Horst asked if Ross could speak on the question of property rights. Ross said that at-will employees can be terminated at will for any reason with no notice required. State employees are terminable for-cause. He said that General Administration had demonstrated an intention to keep the for-cause designation. He noted that EPA employees and most private employees work under the at-will label. With regard to property rights, Ross said that if an interest is brought into an employee agreement, the court calls this a property right. The property right concept brings into play rights such as due process. Jonathan Stephenson noted the case of Still v. Lance and asked whether property rights would be retained if they were not mentioned in statute. Ross said that property rights do not have to be mentioned in statute to be retained. Associate Vice President for Legal Affairs Tom Shanahan reiterated that statures or ordinances are not the only way to create property rights. Moran noted the fear that some employees believe that statutory rights are more difficult to change than policy rights. Ross said that employees will have the most direct input to possible changes. He emphasized that this is not a zero-sum game.
Mary Mackenzie asked if Ross could give other examples of possible changes such as closing for the Christmas through New Year's period. Ross said that the Health Care System uses non-State revenue for salary increases. He said that he could not make promises as the University will take a big hit from the State this year. William Fleming, Vice President for Human Resources, said that one size will not fit all situations at the University. He said that the onus is on the University to improve itself, including in its evaluation system. He noted that the Staff Assembly and other universities will study this question as to what will work best for the University. Jennie Brooks asked whether the Board of Governors would have authority to increase the number of tuition waiver classes back to three from the current two per year. Ross said that this step would take specific legislative action. He noted that the House budget would prohibit the University System from raising tuition for two years, with no bonuses allotted to employees. He said that the University could devise a good personnel system. Carleta Long asked if the University could negotiate with health care companies besides Blue Cross/Blue Shield. Ross said that the bill exempts the State Health plan from the move.
Danny Nguyen asked under what scenario would the personnel flexibility shift benefit front-line employees. Fleming said that the use of non-State funds for staff salaries and increased flexibility in vacation policies represent two areas in which SPA employees would see the benefits of the move. Ashley Fogle asked about increasing parental leave for SPA and EPA non-faculty under this move. Ross said that nothing would prohibit the University from considering this option but costs would play a large factor. Chuck Brink asked if all employees would have policies equally applied under the new system. Ross said that there are differences in administering faculty. He said that there would be no need to keep the designations SPA and EPA non-faculty employees. He said that it was a possibility that the University would move EPA non-faculty employees to just cause. Fleming said that the University would move methodically in order not to contravene federal law. Brink asked if SPA exempt employees would receive overtime or compensatory time under the new plan. Fleming said that these are usually highly paid employees who do not usually earn compensatory time. James Holman asked how the change would affect grievance procedures. Ross said that there could be changes but these would be developed with employees in the process. Fleming envisioned that there would still be campus-based grievance policies that would not change overnight. Laurie Birdsong asked how State service and bonus issues would be affected by the move to personnel flexibility. Ross said that Health Care System employees are still State employees. Associate Vice President of Human Resources Ann Lemmon said that their State service levels would not be affected. Elizabeth Craig noted that Senate Bill 575 had been pulled from consideration by the Legislature. Ross said that the bill would be reintroduced after a technical adjustment. Mackenzie asked if the rationale for the bill was to save money. Ross said that was part of the idea, but these savings will mainly result through saving employee time on redundant procedures. Flexibility will increase morale and should change policies to make the University more attractive to life employees. David Schwarz observed that the University is asking employees to give up something familiar. He asked why the University could not provide a more detailed picture of what would occur. Ross thought that employees would come out worse in the Legislature than within the Board of Governors. Ter Horst thought that the University should articulate a process to staff on how changes would occur. Ross said that the Staff Assembly will form the basis of these changes among many other groups of staff. The bill would take effect at the earliest on January 1, 2012. The proposed changes will not take place without an inclusive process. Ter Horst asked about a meeting schedule and representatives. Ross said that this work would begin in the weeks following the bill's passage. Ross thanked the Forum for the chance to speak and said he would be glad to return at a future date.
Fleming said that the University was trying to strike a reasonable balance between work done before and after passage of the bill. He said that a small taskforce would work on key details of policies that need attention. Employees will have multiple opportunities to provide input to the taskforce which will make recommendations to the Board of Governors. Holman did not want to lose certain rights and asked why they were not written into the bill. Associate Vice President for Legal Affairs Tom Shanahan said that all of the rights that UNC employees now enjoy are undefined in statute but still exist. These rights are instead defined in UNC policies. Senate Bill 575 would transfer authority to the University. Brink asked if policies would be approved across the System. Shanahan said that everyone wants fair and equitable policies across the System, but there are extensive differences between System institutions. He said that the University would not take a one size fits all approach, but would follow broad parameters for fair and equitable policies. The University does not have the same type of jobs that the Department of Corrections or Department of Transportation do.
John Gallo asked if President Ross had reached out to organizations such as the State Employees' Association of North Carolina (SEANC) to discuss changes. Fleming said that UNC System Staff Assembly chair Kelley Eaves-Boykin and UNC-Chapel Hill Employee Forum Chair had met with SEANC leadership and General Administration officials. Shanahan said that he had reached out to SEANC General Counsel to be available to say where their position was wrong, but was not taken up on the offer. Fleming said that other campuses have deliberated about the proposed change. He noted that General Administration had distributed information about the bill to the Staff Assembly before releasing it to the media or even the Governor. Fleming said that there are many situations that the University is trying to deal with all at once given budget cuts already taken and in motion. Jonathan Stephenson said that SEANC General Counsel have fought hard and been successful in the courts. He said that there has been more dialogue than any time in the last ten years. He still worried that without statutory guarantees, employees are relying on little more than a promise. Shanahan said that case law found that property interests are created through statutes, ordinances, and mutually specific understandings. A more current case from the North Carolina Court of Appeals in 2000 reaffirmed the finding that property interests are created in number of different ways. Fleming commented that he had often had situation in which SPA employees seek to become EPA non-faculty but never the reverse. He granted that he did not know the climate here at UNC-Chapel Hill, however. Myra Quick said that constituencies now are concerned about job stability. Fleming noted that his wife serves in an EPA position but had never been at-will.
The Chair noted that the Forum had been instrumental in the change of dial-out prefix from '9' to '7' due to the number of erroneous 911 calls. She noted that the Forum will hold its annual retreat June 1 and will host a community meeting with Chancellor Thorp and Vice Chancellor Malone on June 8.
In the absence of further discussion, the Chair called for a motion to adjourn. Chuck Brink made this motion seconded by Elizabeth Craig. The motion was approved by voice vote and the meeting adjourned at 11:30 a.m.
Matt Banks, Recording Secretary
Employee Forum Book Club: The Silent Patient
February 5, 2020 Employee Forum Meeting
February 18, 2020 Executive Committee Meeting
Employee Forum Book Club: Followers
February 27 @ 12:00 pm - 1:00 pm
March 4, 2020 Employee Forum Meeting
Forum Tweets
HR Update: No budget updates; Paid parental leave rollout is coming very soon, will be retroactive to 1/1; campus c… https://t.co/XJmFyKJa2c 10:03:58 AM January 08, 2020


These Video Segments are for the MAT Independent Study Course and the Online Certificate Renewal Test. To watch a video segment, click the link. Please note that it may take a few minutes for the video to load and begin playing.


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