text:
\section{Introduction}

The electronic health record (EHR) contains critical information for clinicians to assess a patient's medical history (e.g., conditions, laboratory tests, procedures, treatments) and healthcare interactions (e.g., primary care and specialist visits, emergency department visits, and hospitalizations). While medications, labs, and diagnoses are documented through structured data elements and flowsheets, clinical notes contain rich narratives describing the patient's medical condition and interventions. A single hospital visit for a patient with a lengthy hospital stay, or complex illness, can consist of hundreds of notes. At the point of care, clinicians already pressed for time, face a steep challenge of making sense of their patient's documentation and synthesizing it either for their own decision making process or to ensure coordination of care~\citep{hall2004information,ash2004some}.

Automatic summarization has been proposed to support clinicians in multiple scenarios, from making sense of a patient's longitudinal record over long periods of time and multiple interactions with the healthcare system, to synthesizing a specific visit's documentation. Here, we focus on \textit{hospital-course summarization}: faithfully and concisely summarizing the EHR documentation for a patient's specific inpatient visit, from admission to discharge. Crucial for continuity of care and patient safety after discharge~\citep{kripalani2007deficits,van2002effect}, hospital-course summarization also represents an incredibly challenging multi-document summarization task with diverse knowledge requirements.  To properly synthesize an admission, one must not only identify relevant problems, but link them to symptoms, procedures, medications, and observations while adhering to temporal, problem-specific constraints.

Our main contributions are as follows: (1) We introduce the task of hospital-course summarization; (2) we collect a dataset of inpatient documentation and corresponding "Brief Hospital Course" paragraphs extracted from discharge notes; and (3) we assess the characteristics of these summary paragraphs as a proxy for target summaries and discuss implications for the design and evaluation of a hospital-course summarization tool.

\section{Related Works}

Summarization of clinical data and documentation has been explored in a variety of use cases \citep{pivovarov2015automated}. For longitudinal records, graphical representations of structured EHR data elements (i.e., diagnosis codes, laboratory test measurements, and medications) have been proposed~\citep{powsner1997summarizing,plaisant1996lifelines}. Interactive visualizations of clinical problems' salience, whether extracted from notes~\citep{hirsch2015harvest} or inferred from clinical documentation~\cite{levy2020towards} have shown promise~\citep{pivovarov2016can,2020levyfixthesis}.

Most work in this area, however, has focused on clinical documentation of a fine temporal resolution. Traditional text generation techniques have been proposed to synthesize structured data like ICU physiological data streams ~\citep{hunter2008summarising,goldstein2016automated}. \citet{liu2018learning} use a transformer model to write EHR notes from the prior 24 hours, while \citet{liang2019novel} perform disease-specific summarization from individual progress notes.
\citet{mcinerney2020query} develop a distant supervision approach to generate extractive summaries to aid radiologists when interpreting images.
\citet{zhang2018learning, zhang2019optimizing, macavaney2019ontology, sotudeh2020attend} generate the ``Impression'' section of the Radiology report from the more detailed ``Findings'' section.  Finally, several recent works aim to generate EHR notes from doctor-patient conversations~\citep{krishna2020generating,joshi2020dr, MSR_2020}. Recent work on summarizing hospital admissions focuses on extractive methods \citep{moen2014evaluation,moen2016comparison, liu2018unsupervised, alsentzer2018extractive}.

\section{Hospital-Course Summarization Task}

Given the clinical documentation available for a patient hospitalization, our task of interest is to generate a text that synthesizes the hospital course in a faithful and concise fashion.  For our analysis, we rely on the ``Brief Hospital Course'' (BHC), a mandatory section of the discharge note, as a proxy reference.  The BHC tells the story of the patient's admission: \textit{what} was done to the patient during the hospital admission and \textit{why}, as well as the \textit{follow up} steps needed to occur post discharge, whenever needed. Nevertheless, it is recognized as a challenging and time consuming task for clinicians to write~\cite{dodd07,ucirvine20}.

\subsection{Dataset}

To carry out our analysis, we construct a large-scale, multi-document summarization dataset, \textsc{clinsum}\xspace. Materials come from all hospitalizations between 2010 and 2014 at Columbia University Irving Medical Center. \textbf{Table~\ref{tab:clinsum-stats}} shows summary statistics for the corpus. There are a wide range of reasons for hospitalizations, from life-threatening situations (e.g., heart attack) to when management of a specific problem cannot be carried out effectively outside of the hospital (e.g., uncontrolled diabetes). This contributes to the high variance in documentation. For reference, Table~\ref{tab:corpora} provides a comparison of basic statistics to widely used summarization datasets.  Relatively speaking, \textsc{clinsum}\xspace is remarkable for having a very high compression ratio despite having long reference summaries.  Additionally, it appears highly extractive with respect to fragment density (we qualify this in Section \ref{sec:extractiveness}).

Based on advice from clinicians, we rely on the following subset of note types as source documents: ``Admission'', ``Progress'', and ``Consult'' notes. The dataset does not contain any structured data, documentation from past encounters, or other note types (e.g., nursing notes, social work, radiology reports)~\citep{reichert2010cognitive}.  Please refer to Appendix \ref{app:sum-datasets} for more details and rationale.

\begin{table}[t]
    \small
    \centering
    \begin{tabular}{c|l|c|c}
    & \bfseries Variable & \bfseries Value & \bfseries STD \\

    \multirow{3}{*}{Global}
     & \# Patients & 68,936 & \multirow{3}{*}{N/A} \\
     & \# Admissions & 109,726 &  \\
     & \# Source Notes & 2,054,828 &  \\
    \hline
    \multirow{5}{*}{\makecell{Per \\ Adm.}}
    & Length of Stay & 5.8 days & 9.0 \\
    & \# Source Notes & 18.7 & 30.1 \\
    & \# Source Sentences & 1,061.2 & 1,853.6 \\
    & \# Source Tokens & 11,838.7 & 21,506.5 \\
    & \# Summary Sentences & 17.8 & 16.9 \\
    & \# Summary Tokens & 261.9 & 233.8 \\
    \hline
    \multirow{2}{*}{\makecell{Per \\ Sent.}}
    & \# Source Tokens & 10.9 & 12.4 \\
    & \# Summary Tokens & 14.5 & 11.5 \\
    \hline
    \multirow{1}{*}{Ratio}

    & Word Compression & 42.5 & 164.6 \\
    \end{tabular}
    \caption{Basic Statistics for \textsc{clinsum}\xspace.  Value is the total for Global, and average for `Per Admission' and `Per Sentence'. STD is standard deviation.}
    \label{tab:clinsum-stats}
\end{table}

\subsection{Tools for Analysis}

\paragraph{Entity Extraction \& Linking.}
We use the MedCAT toolkit \citep{kraljevic2020multi} to extract medical entity mentions and normalize to concepts from the UMLS (Unified Medical Language System) terminology~\citep{bodenreider2004unified}.  To exclude less relevant entities, we only keep entities from the Disorders, Chemicals \& Drugs, and Procedures semantic groups, or the Lab Results semantic type.

\paragraph{Local Coherence.} We examine inter-sentential coherence in two ways. \textbf{Next-Sentence Prediction (NSP)}. Since we compare across a few datasets representing different domains, we use domain-specific pre-trained BERT models via HuggingFace \citep{wolf2019huggingface}: ``bert-base-cased'' for CNN/DM and Arxiv, ``monologg/biobert\_v1.1\_pubmed'' for Pubmed, and ``emilyalsentzer/Bio\_ClinicalBERT'' for \textsc{clinsum}\xspace. \textbf{Entity-grids.} Entity-grids model local coherence by considering the distribution of discourse entities \citep{barzilay2008modeling}. An entity grid is a 2-D representation of a text whose entries represent the presence or absence of a discourse entity in a sentence.  For our analyses, we treat UMLS concepts as entities and train a neural model, similar to \citet{nguyen2017neural, mohiuddin2018coherence}, which learns to rank the entity grid of a text more highly than the same entity grid whose rows (sentences) have been randomly shuffled.  Please see Appendix \ref{app:coherence} for more details.

\paragraph{Lexical Overlap Metric.} We use ROUGE-1 (R1) \& ROUGE-2 (R2) F-1~\citep{lin2004rouge} to measure lexical overlap, while ignoring higher order variants based on analysis from other work \citep{krishna2021}.  We denote the average of R1 \& R2 scores as $R_{12}$.

\paragraph{Extractive Summarization Baselines.} We rely on a diverse set of sentence extraction methods, whose performance on a held-out portion of \textsc{clinsum}\xspace is reported in Table \ref{tab:big-table}. \textbf{Oracle models} have access to the ground-truth reference and represent upper bounds for extraction.  Here, we define the sentence selection criteria for each oracle variant, leaving more in-depth discussion to the subsequent analysis. \textbf{\textsc{Oracle Top-K}}: Take sentences with highest $R_{12}$ vis-a-vis the reference until a target token count is reached; \textbf{\textsc{Oracle Gain}}: Greedily take source sentence with highest relative $R_{12}$ gain conditioned on existing summary\footnote{This is the Neusum model's objective \citep{zhou2018neural}}. Extract sentences until the change in $R_{12}$ is negative;
\textbf{\textsc{Oracle Sent-Align}}: For each sentence in reference, take source sentence with highest $R_{12}$ score;
\textbf{\textsc{Oracle Retrieval}}: For each sentence in reference, take reference sentence from train set with largest BM25 score \citep{robertson1994some}; and
\textbf{\textsc{Oracle Sent-Align + Retrieval}}: For each sentence in reference, take sentence with highest $R_{12}$ between \textsc{Oracle Sent-Align} and \textsc{Oracle Retrieval}.  We provide two \textbf{unsupervised methods} as well. \textbf{\textsc{Random}}: extracts random sentences until summary reaches target word count (average summary length); \textbf{\textsc{LexRank}}: selects the top-k sentences with largest LexRank \citep{erkan2004lexrank} score until target word count is reached.  For a supervised baseline, we present \textbf{\textsc{ClinNeusum}}: a variant of the Neusum model adapted to the clinical genre~\citep{zhou2018neural}.  \textsc{ClinNeusum} is a hierarchical LSTM network trained on ground-truth labels derived from \textsc{Oracle Gain}, which we detail in Appendix \ref{clin-neusum-details}.

\begin{table*}[ht]
    \centering
    \begin{tabular}{l|ccc|ccc}
    \bfseries Extractive Baseline & \multicolumn{3}{c}{\textbf{ROUGE-1}} & \multicolumn{3}{c}{\textbf{ROUGE-2}} \\
    \hline
     & Recall & Precision & F1 & Recall & Precision & F1  \\
     \hline
    \textsc{Random} & 0.16 & 0.24 & 0.17 & 0.04 & 0.03 & 0.03 \\
    \textsc{LexRank} & 0.18 & 0.21 & 0.18 & 0.05 & 0.05 & 0.05 \\
    \textsc{ClinNeusum} & 0.36 & 0.25 & 0.27 & 0.14 & 0.1 & 0.11 \\
    \textsc{Oracle Top-K} & 0.28 & 0.52 & 0.32 & 0.16 & 0.32 & 0.19 \\
    \textsc{Oracle Gain} & 0.43 & 0.63 & 0.5 & 0.26 & 0.42 & 0.3 \\
    \textsc{Oracle Sent-Align (SA)} & 0.48 & 0.61 & 0.52 & 0.3 & 0.33 & 0.31 \\
    \textsc{Oracle Retrieval} & 0.51 & 0.70 & 0.58 & 0.25 & 0.28 & 0.29 \\
    \textsc{Oracle SA + Retrieval} & \textbf{0.6} & \textbf{0.76} & \textbf{0.66} & \textbf{0.4} & \textbf{0.49} & \textbf{0.43} \\
    \end{tabular}
    \caption{Performance of different sentence selection strategies on \textsc{clinsum}\xspace.}
    \label{tab:big-table}
    \vskip -0.1in
\end{table*}

\section{Dataset Analysis \& Implications}

To motivate future research in multiple, self-contained directions, we distill task-specific characteristics to a few salient, standalone takeaways.  For each takeaway, we provide evidence in the data and/or literature, before proposing implications of findings on model development and evaluation.

\subsection{Summaries are mostly abstractive with a few long segments of copy-pasted text}
\label{sec:extractiveness}

\paragraph{tl;dr.} \textsc{clinsum}\xspace summaries appear extractive according to widely used metrics.  Yet, there is large variance within summaries.  This directly affects the performance of a supervised extractive model, whose selection capability degrades as summary content transitions from copy-paste to abstractive.  In turn, we need models which can handle abrupt transitions between extractive and abstractive text.

\paragraph{Background.} Clinicians copy forward information from previous notes to save time and ensure that each note includes sufficient evidence for billing and insurance purposes \citep{wrenn2010quantifying}.  Copy-paste is both widely used (66-90\% of clinicians according to a recent literature review \citep{tsou2017safe}) and widely applied (a recent study concluded that in a typical note, 18\% of the text was manually entered; 46\%, copied; and 36\% imported\footnote{Imported refers to text typically pulled in from structured data, such as a medication or problem list.}
\citep{wang2017characterizing}). Please see Appendix \ref{app:note-on-copypaste} for more information on the issue of copy-paste.

\paragraph{Analysis - extractiveness.}

\textsc{clinsum}\xspace appears very extractive: a high coverage (0.83 avg / 0.13 std) and a very high density (13.1 avg / 38.0 std) (See \citet{grusky2018newsroom} for a description of the statistics).  However, we find that 64\% of the extractive fragments are unigrams, and 25\% are bigrams, which indicate a high level of re-writing.  The density measure is large because the remaining 11\% of extractive fragments are very long.

Yet, there is a strong positional bias within summaries for long fragments. \textbf{Figure \ref{fig:frag-len-rank}}, groups fragments according to their relative order within each summary.  The longest fragments are usually first.  Qualitative analysis confirms that the beginning of the BHC is typically copied from a previous note and conveys the ``one-liner'' (e.g., \textit{pt is a 50yo male with history of CHF who presents with edema.})

\begin{figure}[ht]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{images/positional.png}}
\caption{Average extractive fragment lengths according to their relative order within the summary.}
\label{fig:frag-len-rank}
\end{center}
\vskip -0.2in
\end{figure}

This abrupt shift in extractiveness should affect content selection.  In particular, when looking at oracle extractive strategies, we should see clear-cut evidence of \textbf{(1)} 1-2 sentences which are easy to identify as salient (i.e., high lexical overlap with source due to copy-paste), \textbf{(2)} a murkier signal thereafter.  To confirm this, we analyze the sentences selected by the \textsc{Oracle Gain} method, which builds a summary by iteratively maximizing the $R_{12}$ score of the existing summary vis-a-vis the reference.

In Figure \ref{fig:rouge-by-step}, two supporting trends emerge.  \textbf{(1)} On average, one sentence accounts for roughly 50\%\footnote{From Table \ref{tab:big-table}, the average $R_{12}$ score is 0.39 for \textsc{Oracle Gain}.  To reconcile this number with respect to Figure \ref{fig:rouge-by-step}, we note that the average oracle summary is far less than the 20 sentence upper bound shown in the chart.} of the overall $R_{12}$ score. \textbf{(2)} Afterwards, the marginal contribution of the next shrinks, as well as the $R_{12}$ gap between the best sentence and the minimum / average, according to the oracle.

\begin{figure}[ht]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{images/rouge_by_extractive_step.png}}
\caption{We plot average ROUGE score as summaries are greedily built by adding the sentence with the highest relative ROUGE gain vis-a-vis the current summary, until the gain is no longer positive (\textsc{Oracle Gain}).  We also include the difference between the highest scoring sentence and the average / minimum to demonstrate a weakening sentence selection signal after the top 1-2. }
\label{fig:rouge-by-step}
\end{center}
\vskip -0.2in
\end{figure}

There should also be evidence of the copy-paste positional bias impacting content selection.  Table \ref{tab:pos-rank-by-step} reveals that the order in which the \textsc{Oracle Gain} summary is built--by maximal lexical overlap with the partially built summary--roughly corresponds to the true ordering of the summary.  More simply, the summary transitions from extractive to abstractive.

\begin{table}[ht]
    \centering
    \begin{tabular}{c|c}
    \bfseries Extractive & \bfseries Average Rank of Closest \\
    \bfseries Step & \bfseries Reference Sentence \\
    1 & 4.7 \\
    2 & 6.0 \\
    3 & 6.3 \\
    4 & 6.7 \\
    5 & 7.3 \\
    > 5 & 10.1 \\
    \end{tabular}
    \caption{\textsc{Oracle Gain} greedily builds summaries by repeatedly selecting the sentence which maximizes the $R_{12}$ score of the partially built summary.  By linking each extracted sentence to its closest in the reference, we show that this oracle order is very similar to the true ordering of the summary.}
    \label{tab:pos-rank-by-step}
\end{table}

Unsurprisingly, a model (\textsc{ClinNeusum}) trained on \textsc{Oracle Gain} extractions gets progressively worse at mimicking it. Specifically, for each extractive step, there exists a ground-truth ranking of candidate sentences by relative $R_{12}$ gain.  As the relevance gap between source sentences shrinks (from Figure \ref{fig:rouge-by-step}), \textsc{ClinNeusum}'s predictions deviate further from the oracle rank (Table \ref{tab:rank-by-step}).

\begin{table}[ht]
    \centering
    \begin{tabular}{c|c|c}
    \bfseries Extractive & \multicolumn{2}{c}{\bfseries Ground Truth Rank} \\
    \bfseries Step & \bfseries Average & \bfseries Median  \\
    1 & 28 & 7 \\
    2 & 69 & 22 \\
    3 & 74 & 31 \\
    4 & 79 & 39 \\
    5 & 76 & 42 \\
    > 5 & 80 & 60 \\
    \end{tabular}
    \caption{Rank of selected sentence vis-a-vis oracle rank at each extraction step.  A perfectly trained system would have a ground-truth of 1 at each step.}
    \label{tab:rank-by-step}
    \vskip -0.2in
\end{table}

\paragraph{Analysis - Redundancy.} Even though we prevent all baseline methods from generating duplicate sentences (23\% of source sentences have exact match antecedents), there is still a great deal of redundancy in the source notes (i.e., modifications to copy-pasted text).  This causes two issues related to content selection. The first is fairly intuitive - that local sentence extraction propagates severe redundancy from the source notes into the summary and, as a result, produces summaries with low lexical coverage.  We confirm this by examining the performance between the \textsc{Oracle Top-K} and \textsc{Oracle Gain}, which represent summary-unaware and summary-aware variants of the same selection method.  While both extract sentences with the highest $R_{12}$ score, \textsc{Oracle Gain} outperforms because it incorporates redundancy by considering the relative $R_{12}$ gain from an additional sentence.

The second side effect is perhaps more surprising, and divergent from findings in summarization literature.  For most corpora, repetition is indicative of salience.  In fact, methods based on lexical centrality, i.e., TextRank \citep{mihalcea2004textrank} and LexRank \citep{erkan2004lexrank}, still perform very competitively for most datasets. Yet, for \textsc{clinsum}\xspace, LexRank barely outperforms a random baseline.  Poor performance is not only due to redundance, but also a weak link between lexical centrality and salience. The Pearson correlation coefficient between a sentence's LexRank score and its $R_{12}$ overlap with the reference is statistically significant ($p=0$) yet weak ($r=0.29$).

Qualitative analysis reveals two principal reasons, both related to copy-paste and/or imported data.  The first relates to the propagation of frequently repeated text which may not be useful for summaries: administrative (names, dates), imported structured data, etc.  The second relates to sentence segmentation. Even though we use a custom sentence splitter, our notes still contain some very long sentences due to imported lists and semi-structured text--a well-documented issue in clinical NLP \citep{leaman2015challenges}. LexRank summaries have a bias toward these long sentences (26.2 tokens versus source average of 10.9), which have a greater chance of containing lexical centroid(s).

To bypass some of these issues, however, one can examine the link between centrality and salience at the more granular level of entities. \textbf{Figure \ref{fig:source-freq-recall}} shows a clear-cut positive correlation between source note mention frequency of UMLS concepts and the probability of being included in the summary.

\begin{figure}
\centering
\begin{center}
\includegraphics[width=\columnwidth]{images/source_freq_recall.png}
\caption{Relationship between source entity mentions and probability of inclusion in the summary.
}
\label{fig:source-freq-recall}
\end{center}
\vskip -0.2in
\end{figure}

\paragraph{Implications.}

Regarding within-summary variation in \textbf{extractiveness}, we argue for a hybrid approach to balance extraction and abstraction. One of the most widely-used hybrid approaches to generation is the Pointer-Generator (PG) model \citep{see2017get}, an abstractive method which allows for copying (i.e., extraction) of source tokens.
Another research avenue explicitly decouples the two. These extract-then-abstract approaches come in different flavors: sentence-level re-writing \citep{chen2018fast, bae2019summary}, multi-sentence fusion \citep{lebanoff2019scoring}, and two-step disjoint extractive-abstracive steps \citep{mendes2019jointly}.

While highly effective in many domains, these approaches do not consider systematic differences in extractiveness within a single summary. To incorporate this variance, one could extend the PG model to copy pre-selected long snippets of text.  This would mitigate the problem of copy mechanisms learning to copy very long pieces of text \citep{gehrmann2018bottom} - undesirable for the highly abstractive segments of \textsc{clinsum}\xspace.  Span-level extraction is not a new idea \citep{xu2020discourse}, but, to our knowledge, it has not been studied much in otherwise abstractive settings.  For instance, \citet{joshi2020dr} explore patient-doctor conversation summarization and add a penalty to the PG network for over-use of the generator, yet this does not account for intra-summary extractiveness variance.

Regarding \textbf{redundancy}, it is clear that, in contrast to some summarization tasks \citep{kedzie2018content}, summary-aware content selection is essential for hospital course summarization.  Given so much noise, massive EHR and cite-specific pre-processing is necessary to better understand the signal between lexical centrality and salience.

\subsection{Summaries are concise yet comprehensive} \label{sec:concise}

\paragraph{tl;dr.} BHC summaries are packed with medical entities, which are well-distributed across the source notes. As such, relations are often not explicit.  Collectively, this difficult task calls for a domain-specific approach to assessing faithfulness.

\paragraph{Analysis - concise}

We find that summaries are extremely dense with medical entities: $20.9\%$ of summary words are medical UMLS entities, compared to $14.1\%$ in the source notes.  On average, summaries contain $26$ unique entities whereas the source notes contain $265$ --- an entity compression ratio of 10 (versus token-level compression of 43).

\paragraph{Analysis - comprehensive.}

Many summarization corpora exhibit systematic biases regarding where summary content can be found within source document(s) \citep{dey2020corpora}. On \textsc{clinsum}\xspace, we examine the distribution of entities along two dimensions: \textit{macro} considers the differences in entity share across notes, and \textit{micro} considers the differences within each note (i.e., lead bias). \textbf{(1) Macro Ordering.} When looking at the source notes one by one, how much \textit{additional} relevant information (as measured by entities present in the summary) do you get from each new note?  We explore three different orderings: (1) \textsc{Forward} orders the notes chronologically, (2) \textsc{backward} the reverse, and (3) \textsc{Greedy Oracle} examines notes in order of decreasing entity entity overlap with the target.  Given the large variation in number of notes per admission, we normalize by binning notes into deciles. \textbf{Figure \ref{fig:chronology}} shows that it is necessary to read the entire set of notes despite diminishing marginal returns.  One might expect the most recent notes to have the most information, considering present as well as copy-forwarded text. Surprisingly, \textsc{forward} and \textsc{backward} distributions are very similar.  \textsc{Greedy Oracle} gets at the level of information concentration.  On average, the top 10\% of most informative notes cover just over half of the entities found in the summary. We include absolute and percentage counts in Table \ref{tab:ordering}.  \textbf{(2) Micro Ordering.} We plot a normalized histogram of summary entities by relative position within the source documents. Figure \ref{fig:lead-bias} reveals a slight lead bias, followed by an uptick toward the end.  Clinical notes are organized by section: often starting with the past medical history and present illness, and typically ending with the plan for future care.  All are needed to write a complete BHC.

\begin{table}[ht]
    \centering
    \begin{tabular}{l|c|c|c}
    & \multicolumn{2}{c}{Avg Notes to Read} \\
    \bfseries Ordering & \bfseries Number & \bfseries Percent \\
    \textsc{Forward} & 8.5 & 0.80 \\
    \textsc{Backward} & 7.8 & 0.73 \\
    \textsc{Greedy Oracle} & 5.0 & 0.50 \\
    \end{tabular}
    \caption{Number of documents necessary to cover all relevant UMLS entities---present in the summary---according to three different ordering strategies. \textsc{Forward} orders the notes chronologically, \textsc{Backward} the reverse, and \textsc{Greedy Oracle} examines notes in order of decreasing entity overlap with the target.}
    \label{tab:ordering}
    \vskip -0.2in
\end{table}

\begin{figure}[!ht]
\centering
\begin{center}
\includegraphics[width=\columnwidth]{images/source_ent_placement_macro.png}
\caption{The average fraction of additional relevant UMLS entities---present in the summary---from reading a patient's visit notes.
\textsc{Forward} orders the notes chronologically, \textsc{Backward} the reverse, and \textsc{Greedy Oracle} in order of decreasing entity overlap.}
\label{fig:chronology}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[!ht]
\centering
\begin{center}
\includegraphics[width=\columnwidth]{images/entity_lead_bias.png}
\caption{The distribution of relevant entities---present in the summary---within an average source note. Source Note Decile refers to the relative position of each mention within a note. Relevant entities appear throughout an average note, with a slight lead bias.}
\label{fig:lead-bias}
\end{center}
\vskip -0.2in
\end{figure}

\paragraph{Implications.}

The fact that entities are so densely packed in summaries makes models more susceptible to factual errors that misrepresent complex relations. On the CNN/DailyMail dataset, \citet{goel2021robustness} reveal performance degradation as a function of the number of entities.  This is magnified for clinical text, where failure to identify which treatments were tolerated or discontinued, or to differentiate conditions of the patient or family member, could lead to serious treatment errors.

Recently, the summarization community has explored fact-based evaluation.  Yet, many of the proposed methods treat global evaluation as the independent sum of very local assessments.  In the case of QA-based methods, it is a quiz-like aggregation of individual scores to fairly narrow questions that usually seek to uncover the presence or absence of a single entity or relation.  Yet, factoid \citep{chen2018semantic}, cloze-style \citep{eyal2019question, scialom2019answers, deutsch2020towards}, or mask-conditioned question generation \citep{durmus2020feqa} may not be able to directly assess very fine-grained temporal and knowledge-intensive dependencies within a summary.  This is a natural byproduct of the fact that many of the factuality assessments were developed for shorter summarization tasks (i.e., headline generation) in the news domain \citep{cao2018faithful,kryscinski2019neural,maynez2020faithfulness}.  Entailment-based measures to assess faithfulness \citep{pasunuru2018multi, welleck2018dialogue} can capture complex dependencies yet tend to rely heavily on lexical overlap without deep reasoning \citep{falke2019ranking}.

Taken together, we argue for the development of fact-based evaluation metrics which encode a deeper knowledge of clinical concepts and their complex semantic and temporal relations\footnote{\citet{zhang2019optimizing} directly address factuality of clinical text, yet the setting is very different.  They explore radiology report accuracy, which is not a temporal multi-document summarization task.  Additionally, they rely on a smaller IE system tailored specifically for radiology reports \citep{irvin2019chexpert}.}.

\subsection{Summaries have different style and content organization than source notes} \label{misalignment}

\paragraph{tl;dr.} Hospital course summarization involves not only massive compression, but a large style and organization transfer. Source notes are written chronologically yet the way clinicians digest the information, and write the discharge summary, is largely problem-oriented.  With simple oracle analysis, we argue that retrieve-edit frameworks are well-suited for hospital course generation.

\paragraph{Analysis - Style.}  Clinical texts contain many, often obscure, abbreviations \citep{finley2016towards, adams2020zero}, misspellings, and sentence fragments~\citep{demner2009can}. Using a publicly available abbreviation inventory \citep{moon2014sense}, we find that abbreviations are more common in the BHC.  Furthermore, summary sentences are actually longer on average than source sentences (15.8 versus 12.4 words).

\paragraph{Analysis - Organization.}

Qualitative analysis confirms that most BHCs are written in a problem-oriented fashion \citep{weed1968medical}, i.e., organized around a patient's disorders.  To more robustly analyze content structure, we compare linked UMLS entities at the semantic group level: \textsc{Drugs}, \textsc{Disorders}, and \textsc{Procedures} \citep{mccray2001aggregating}. In particular, we compare \textbf{global} proportions of semantic groups, \textbf{transitions} between entities, as well as \textbf{positional} proportions within summaries.  \textbf{(1) Global.} Procedures are relatively more prevalent in summaries (31\% versus 24\%), maybe because of the emphasis on events happening during the hospitalization. In both summary and source notes, \textsc{Disorders} are the most prevalent (54\% and 46\%, respectively).  Drugs make up 23\% and 22\% of entity mentions in summary and source notes, respectively.  \textbf{(2) Transitions.} From both source and summary text, we extract sequences of entities and record adjacent transitions of their semantic groups in a $3\times3$ matrix. \textbf{Figure \ref{fig:ent-trans}} indicates that summaries have fewer clusters of semantically similar entities (diagonal of the transition matrix).  This transition matrix suggests a problem-oriented approach in which disorders are interleaved with associated medications and lab results.  \textbf{(3) Positional.}  Finally, within summaries, we examine the positional relative distribution of semantic groups and connect it to findings from Section \ref{sec:extractiveness}. In \textbf{Figure \ref{fig:sem-group-positional}}, we first compute the start index of each clinical entity, normalized by the total length, and then group into ten equally sized bins.  The early prevalence of disorders and late prevalence of medications is expected, yet the difference is not dramatic.  This suggests an HPI-like statement up front, followed by a problem oriented narrative.

\begin{figure}[ht]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{images/sem_group_positional_distribution.png}}
\caption{Position of entities within a summary.}
\label{fig:sem-group-positional}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}
\centering
\begin{center}
\includegraphics[width=\columnwidth]{images/heatmap.jpg}
\caption{Entity Transition Matrices for source notes and target summaries. Summaries have fewer clusters of semantically similar entities,  indicating that entity mentions are woven into a problem-oriented summary.}
\label{fig:ent-trans}
\end{center}
\vskip -0.2in
\end{figure}

If there is a material transfer in \textbf{style} and \textbf{content}, we would expect that summaries constructed from other summaries in the dataset would have similar or better lexical coverage than summaries constructed from sentences in the source notes.  To assess this, we compare two oracle baselines, \textsc{sent-align} and \textsc{retrieval}. For each sentence in the summary, we find its closest corollary either in the source text (\textsc{sent-align}) or in other summaries in the dataset (\textsc{retrieval}). While the retrieval method is at a distinct disadvantage because it does not contain patient-specific information and retrieval is performed with BM25 scores, we find both methods yield similar results (\textbf{Table \ref{tab:big-table}}).  An ensemble of \textsc{sent-align} and \textsc{retrieval} performs better than either alone, suggesting that the two types of sources may be complementary.  82\% of this oracle's summary sentences are retrievals. Summaries adapt the style and problem-oriented structure of other summaries, but contain patient-specific information from the source notes.

\begin{figure}[ht]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{images/nsp_single.png}}
\caption{NSP logit by relative position of the next sentence across summaries for several datasets. An offset of 1 corresponds to the true next sentence. }
\label{fig:nsp}
\end{center}
\vskip -0.2in
\end{figure}

\paragraph{Implications.}

Hospital-course summaries weave together disorders, medications, and procedures in a problem-oriented fashion.  It is clear that substantial re-writing and re-organization of source content is needed. One suitable approach is to use the retrieve-rerank-rewrite ($R^3$) framework proposed by \citet{cao2018retrieve}.  To support this notion, more recent work demonstrates that retrieval augmented generation is effective for knowledge-intensive tasks \citep{lewis2020retrieval}, enhances system interpretability \citep{guu2020realm,krishna2020generating}, and can improve LM pre-training \citep{lewis2020pre}\footnote{The related idea of template-based generation has gained traction within the probabilistic community \citep{wiseman2018learning, guu2018generating, wu2019response, he2020learning}.}.  Also, efforts to bridge the gap between template-based and abstractive generation have been successful in the medical domain for image report generation \citep{li2018hybrid}.

In this light, BHC generation could be truly problem-oriented.  The first step would involve selecting salient problems (i.e., disorders) from the source text--a well-defined problem with proven feasibility \citep{van2010corpus}. The second step would involve separately using each problem to retrieve problem-specific sentences from other summaries. These sentences would provide clues to the problem's relevant medications, procedures, and labs. In turn, conceptual overlap could be used to re-rank and select key, problem-specific source sentences.  The extracted sentences would provide the patient-specific facts necessary to re-write the problem-oriented retrieved sentences.

\subsection{Summaries exhibit low lexical cohesion} \label{cohesion}

\paragraph{tl;dr.} Lexical cohesion is sub-optimal for evaluating hospital-course discourse because clinical summaries naturally exhibit frequent, abrupt topic shifts.  Also, low correlation exists between lexical overlap and local coherence metrics.

\paragraph{Analysis.} Entity-based coherence research posits that "texts about the same discourse entity are perceived to be more coherent than texts fraught with abrupt switches from one topic to the next" \citep{barzilay2008modeling}.  Yet, for \textsc{clinsum}\xspace summaries, coherence and abrupt topic shifts are not mutually exclusive. An analysis of the entity grids of summaries, presumably coherent, are sparse, with few lexical chains. In fact, over 66\% of the entities in the BHC appear only once.  Of those with multiple mentions, the percentage which appear in adjacent sentences is only 9.6\%.  As in \citet{prabhumoye2020topological}, we also compare coherence with next-sentence prediction (NSP). \textbf{Figure \ref{fig:nsp}} plots the NSP logit by positional offset, where an offset of 1 corresponds to the next sentence, and -1 to the previous. NSP relies on word overlap and topic continuity \citep{bommasani2020intrinsic}, so it makes sense it is lowest for \textsc{clinsum}\xspace.

To confirm the hypothesis that ROUGE does not adequately capture content structure, we use the \textit{pairwise ranking} approach to train and evaluate an entity-grid based neural coherence model~\cite{barzilay2008modeling,nguyen2017neural}.
\textbf{Table \ref{tab:pra}} shows ROUGE and coherence metrics side-by-side for \textsc{Oracle Gain}, which naively orders sentences according to document timestamp, then within-document position, and \textsc{Oracle Sent-Align}, which maintains the structure of the original summary. The poor coherence of \textsc{Oracle Gain} is obscured by comparable ROUGE scores.

\begin{table}[ht]
    \centering
    \begin{tabular}{l|c|c|c}
    \bfseries Summary & \bfseries Acc. & \bfseries R1 & \bfseries R2 \\
    Actual Summary & 0.86 & N/A & N/A \\
    \textsc{Oracle Sent-Align} & 0.75 & 0.52 & 0.30 \\
    \textsc{Oracle Gain} & 0.54 & 0.48 & 0.30 \\
    \end{tabular}
    \caption{Comparison of coherence and ROUGE. Acc. refers to pair-wise ranking accuracy from scoring summaries against random permutations of themselves. }

    \label{tab:pra}
    \vskip -0.2in
\end{table}

\paragraph{Implications.}

Content organization is critical and should be explicitly evaluated.  A well-established framework for assessing organization and readability is coherence.  A large strand of work on modeling coherent discourse has focused on topical clusters of entities \citep{azzam1999using, barzilay2002inferring, barzilay2004catching, okazaki2004improving}.  Yet, as shown above, \textsc{clinsum}\xspace summaries exhibit abrupt topic shifts and contain very few repeated entities.  The presence and distribution of lexical \citep{morris1991lexical, barzilay1999using} or co-referential \citep{azzam1999using} chains, then, might not be an appropriate proxy for clinical summary coherence.  Rather, we motivate the development of problem-oriented models of coherence, which are associative in nature, and reflect a deeper knowledge about the relationship between disorders, medications, and procedures. The impetus for task-tailored evaluation metrics is supported by recent meta analyses \citep{fabbri2020summeval, bhandari2020re}.

\subsection{BHC summaries are silver-standard} \label{silver-standard}
\paragraph{tl;dr.}  Discharge summaries and their associated BHC sections are frequently missing critical information or contain excessive or erroneous content.  Modeling efforts should address sample quality.

\paragraph{Analysis.}
\citet{kripalani2007deficits} find that discharge summaries often lack important information including diagnostic test results (33-63\% missing) treatment or hospital course (7-22\%), discharge medications (2-40\%), test results pending at discharge (65\%), patient/family counseling (90-92\%), and follow-up plans (2-43\%). The quality of the reporting decreases as the length of the discharge summary increases, likely due to copy-pasted information~\citep{van1999necessary}.

These quality issues occur for a number of reasons: (1) limited EHR search functionality makes it difficult for clinicians to navigate through abundant patient data~\citep{christensen2008instant}; (2) multiple clinicians contribute to incrementally documenting care throughout the patient's stay; (3) despite existing guidance for residents, clinicians receive little to no formal instruction in summarizing patient information~\citep{ming2019discharge}; and (4) clinicians have little time for documenting care.

\paragraph{Implications.}

Noisy references can harm model performance, yet there is a rich body of literature to show that simple heuristics can identify good references \citep{bommasani2020intrinsic} and/or filter noisy training samples \citep{rush2015neural, akama2020filtering, matsumaru2020improving}. Similar strategies may be necessary for hospital-course generation with silver-standard data.  Another direction is scalable reference-free evaluations \citep{shafieibavani2018summarization, narayan2019highres, sellam2020bleurt, gao2020supert, vasilyev2020fill}.

\section{Conclusion}

Based on a comprehensive analysis of clinical notes, we identify a set of implications for hospital-course summarization on future research.  For modeling, we motivate \textbf{(1)} the need for dynamic hybrid extraction-abstraction strategies (\ref{sec:extractiveness}); \textbf{(2)} retrieval-augmented generation (\ref{misalignment}); and \textbf{(3)} the development of heuristics to assess reference quality (\ref{silver-standard}).  For evaluation, we argue for \textbf{(1)} methods to assess factuality and discourse which are associative in nature, i.e., incorporate the complex inter-dependence of problems, medications, and labs (\ref{sec:concise}, \ref{cohesion}); and \textbf{(2)} scalable reference-free metrics (\ref{silver-standard}).

\section{Ethical Considerations}
\xhdr{Dataset creation}
Our \textsc{clinsum}\xspace dataset contains protected health information about patients. We have received IRB approval through our institution to access this data in a HIPAA-certified, secure environment. To protect patient privacy, we cannot release our dataset, but instead describe generalizable insights that we believe can benefit the general summarization community as well as other groups working with EHR data.

\xhdr{Intended Use \& Failure Modes}
The ultimate goal of this work is to produce a summarizer that can generate a summary of a hospital course, and thus support clinicians in this cognitively difficult and time-consuming task. While this work is a preface to designing such a tool, and significant advances will be  needed to achieve the robustness required for deployment in a clinical environment, it is important to consider the ramifications of this technology at this stage of development. We can learn from existing clinical summarization deployed~\cite{pivovarov2016can} and other data-driven clinical decision support tools~\cite{chen2020ethical}. As with many NLP datasets, \textsc{clinsum}\xspace likely contains biases, which may be perpetuated by its use.
There are a number of experiments we plan to carry out to identify documentation biases and their impact on summarization according to a number of dimensions such as demographics (e.g., racial and gender), social determinents of health (e.g., homeless individuals), and clinical biases (e.g., patients with rare diseases).
Furthermore, deployment of an automatic summarizer may lead to automation bias~\citep{goddard2012automation}, in which clinicians over rely on the automated system, despite controls measures or verification steps that might be built into a deployed system. Finally, medical practices and EHRs systems constantly change, and this distribution drift can cause models to fail if they are not updated. As the NLP community continues to develop NLP applications in safety-critical domains, we must carefully study how can can build robustness, fairness, and trust into these systems.

\section*{Acknowledgements}

We thank Alex Fabbri and the NAACL reviewers for their constructive, thoughtful feedback. This work was supported by NIGMS award R01 GM114355 and NCATS award U01 TR002062.

redpajama_set_name: RedPajamaArXiv


You wish to assign a tape library to the media management server. The tape library must be connected to a system controlled by the software.
2. Select the media management server you wish to use.
⇨ The New Library dialog is opened.
4. In the Server field, select the library server.
⇨ After the server has been selected, the system searches for hardware of type Tape Library. The library server is the system to which the tape library is connected and on which the driver for the control of the tape library is installed. If more than one library is found, a shortcut menu is displayed under Library control. The data of the individual libraries are shown in the section below.
5. Select a Library Control.
6. In the Library name field, enter a name for the library.
⇨ The name is stored in the library/media database.
7. Confirm by clicking [OK].
The new library is created on the selected media management server.


text:
Tutorial
========

Example HTML
------------
Throughout this tutorial, we'll use the following simple web page with a form to
demonstrate the page objects pattern.

.. code-block:: html

    <html>
        <head>
            <title>Login Page</title>
        </head>
        <body>
            <form type="POST" action="/login">
                <input type="text" name="username" id="user-input"></input>
                <input type="password" name="password"></input>
                <input type="submit">Submit</input>
            </form>
        </body>
    </html>

Let's assume also that this page is hosted on the url http://example.com/login

A simple Page Object
--------------------

Here's a simple page object that models this page.

.. code-block:: python

    >>> from page_objects import PageObject, PageElement
    >>>
    >>> class LoginPage(PageObject):
            username = PageElement(id_='user-input')
            password = PageElement(name='password')
            login = PageElement(css='input[type="submit"]')
            form = PageElement(tag_name='form')

What is a Page Object?
----------------------

It is a class that, when instantiated, models a single page on your website.
It has attributes on it that model elements on the page. Accessing the attributes
on the Page Object instance accesses the live elements on the page, thus removing
the need for your test code to worry about how to go about finding them.

In our example, the ``LoginPage`` class is how the test will access the login page
HTML, it has three attributes of type ``PageElement`` that refer to the three
input elements, and one for the form.

Page Elements and locators
--------------------------

Page Elements allow you to specify in exactly one place how to find a particular
element on your page. They are implemented using the Descriptor protocol which
you can read more about here: https://docs.python.org/3.4/howto/descriptor.html.
When constructing a Page Element, you specify the locator for the element, which
can be any from the following table:

+--------------------+--------------------------------------+
| Keyword Arg        | Description                          |
+====================+======================================+
| id_                | Element ID attribute                 |
+--------------------+--------------------------------------+
| css                | CSS Selector                         |
+--------------------+--------------------------------------+
| name               | Element name attribute               |
+--------------------+--------------------------------------+
| class_name         | Element class name                   |
+--------------------+--------------------------------------+
| tag_name           | Element HTML tag name                |
+--------------------+--------------------------------------+
| link_text          | Anchor Element text content          |
+--------------------+--------------------------------------+
| partial_link_text  | Anchor Element partial text content  |
+--------------------+--------------------------------------+
| xpath              | XPath                                |
+--------------------+--------------------------------------+

These map directly to Selenium Webdriver's element accessor API which is documented
here: http://selenium-python.readthedocs.org/en/latest/locating-elements.html

Using Page Objects
------------------

Page Objects are constructed with an instantiated webdriver instance, and
optinally a root URI:

.. code-block:: python

    >>> from selenium import webdriver
    >>> driver = webdriver.PhantomJS()
    >>> page = LoginPage(driver, root_uri="http://example.com")

Here I've used the PhantomJS webdriver which is a convenient way to test your
site without needing a full browser stack installed.  The root URI is purely
to provide a base for the Page Object's one and only method, ``get()``:

.. code-block:: python

    >>> page.get('/login')

This call above instructs the browser to load the url http://example.com/login.

Accessing Page Elements
------------------------

To access elements on the page we *get* attributes on the page object. This will
return a Selenium ``WebElement`` instance for the selector that was specified
in the PageElement's constructor.  If the element is not found, it will return
``None``.

For example, to check that the form above was using POST instead of GET, we would
do the following.

.. code-block:: python

    >>> page.form
    <selenium.webdriver.remote.webelement.WebElement object at 0x2b089299a510>
    >>> assert page.form.get_attribute('type') == 'POST'

Here, accessing ``page.form`` gets the form element off of the page, allowing us
to run the ``WebElement.get_attribute`` function to return its form type.

Interacting with Page Elements
------------------------------

We can interact with page elements in our tests as well. To type in text inputs,
we can *set* attributes on the Page Object. This sends the text that we set on the
attribute to the Selenium ``WebElement`` using its ``send_keys`` method.

For example, to fill in the form above, and then click the login button we would do
the following:

.. code-block:: python

    >>> page.username = 'secret'
    >>> page.password = 'squirrel'
    >>> page.login.click()

Multi Page Elements
-------------------

Sometimes we we want to access lists of elements from the page. To do this there is
a ``MultiPageElement`` class which is constructed exactly the same as ``PageElement``.

.. code-block:: python

    >>> from page_objects import PageObject, MultiPageElement
    >>>
    >>> class LoginPage(PageObject):
            inputs = MultiPageElement(tag_name='input')

When accessed, they return a list of the matching elements, or an empty list if there
was nothing found.

.. code-block:: python

    >>> page.inputs
    [<selenium.webdriver.remote.webelement.WebElement object at 0x2b089299a510>,
     <selenium.webdriver.remote.webelement.WebElement object at 0x2b089299a520>,
     <selenium.webdriver.remote.webelement.WebElement object at 0x2b089299a530>]

You can send text to them all as well:

.. code-block:: python

    >>> page.inputs = 'squirrels'

Elements with context
---------------------

By default, when the ``PageElement`` objects are searching on the page for their matching
selector, they are searching from the root of the DOM. Or to put it another way, they
are searching across the whole page. Sometimes, it might be better to search within
the context of another element if you have many similar items on the page.

Take this example where there are more than one login form on the page:

.. code-block:: html

    <html>
        <body>
            <form id="login-1" type="POST" action="/login-1">
                <input type="text" name="username"></input>
                <input type="password" name="password"></input>
                <input type="submit">Submit</input>
            </form>
            <form id="login-2" type="POST" action="/login-2">
                <input type="text" name="username"></input>
                <input type="password" name="password"></input>
                <input type="submit">Submit</input>
            </form>
        </body>
    </html>

You could have separate page elements for each form input, like this:

.. code-block:: python

    >>> class LoginPage(PageObject):
            submit_1 = PageElement(css='#form-1 input[type="submit"]')
            submit_2 = PageElement(css='#form-2 input[type="submit"]')

However, you can also construct the page elements with the ``context`` flag set like this:

.. code-block:: python

    >>> class LoginPage(PageObject):
            form1 = PageElement(id_='form-1')
            form2 = PageElement(id_='form-1')
            submit = PageElement(css='input[type="submit"]', context=True)

This allows you to access the submit element *within* a form element, by calling
the submit element like you would a method:

.. code-block:: python

    >>> page.submit(page.form1).click()

In this way, Page Elements with context are like 'saved searches'.

Accessing the Webdriver directly
--------------------------------

You can always access the webdriver that the Page Object was constructed with
direcly, using the ``w`` attribute.

.. code-block:: python

    >>> page.w
    <selenium.webdriver.phantomjs.webdriver.WebDriver object at 0x2b0891af39d0>
    >> page.w.current_url
    'http://example.com/login'

redpajama_set_name: RedPajamaGithub


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What is a "variation"? And is it any different from a "running change"?
"Variations," sometimes also known as "running changes," are changes made to a toy through its production cycle. Some changes are intentional (so a company can offer you a different product) and are often referred to as a "variation." An example would be a toy that employs one mold that's painted numerous ways in order to provide a complete army. Variations are sometimes but not always created with the collector in mind.
"Running changes" are alterations made to a toy due to available materials, aesthetics, marketing, or safety concerns. An example is the short and long lightsaber issue from 1996 or revising packaging to have the most up-to-date figures on the back of the package. While these toys with running changes are not usually intended to be collectibles, they often become them.
At Entertainment Earth, we ship you the toys we receive right from the manufacturer. So, you receive the same figures we do. Whether it's a common version or a special rare treat, you're not charged a premium for an assortment containing a special toy or collectible.


text:
Some think the' Big Apple' Mayor Bill de Blasio is quietly running for President….
The guy seems to be going to all the places that a 2002 Democratic Presidential candidate would be going to….
He's a certified Progressive…
He running the biggest city in America….
Hmmmmm?
He has traveled to three early presidential primary states, held $5,000-a-plate events for donors and hired a small-dollar fund-raising consultant.
He spoke at the American Israel Public Affairs Committee, a pro-Israel lobbying group, and is now on another trip to raise money in Boston, after which he will meet with Democratic activists in Las Vegas.
These are the concrete steps of a man preparing to run for president.
But thus far, that man, Mayor Bill de Blasio of New York City, would prefer that his words speak louder than his actions. When asked if he plans to run for president, he gives the same answers. He is "not ruling anything out," and will consult with his family before making a decision, one that he says will come "sooner than later."
Bank on sooner.
Mr. de Blasio has had two City Hall aides working on his tentative campaign on their own time; one of them, Mike Casca, is a veteran of Senator Bernie Sanders's 2016 presidential campaign, and worked in the senator's office.
The other mayoral aide helping Mr. de Blasio with his 2020 exploration is Jon Paul Lupo; last month, Mr. Lupo brought aboard Trilogy Interactive, a Washington, D.C.-based company that does digital fund-raising and marketing for political campaigns and others, to assist the mayor's national political action committee, Fairness PAC.
Mr. de Blasio created Fairness PAC to fund national Democratic candidates, and to pay for his and his wife's political travel outside of New York. The fund raised just over $600,000 last year and has spent almost as much…..
image…NY Post
Filed Under: Politics Tagged With: 2020 Democratic Early Presidential nomination field, Bill de Blasio, Politics

redpajama_set_name: RedPajamaCommonCrawl


Owen Wilson stars in "Drillbit Taylor," a PG-13 comedy about a homeless guy pretending to be a bodyguard to make some money off of three wimpy kids.It is the first day of high school, and Ryan (Troy Gentile) and Wade (Nate Hartley) are positive that they will be popular this year. However, that is immediately hard to believe, considering they show up to school wearing the same shirt. It all goes downhill from there. When Wade sticks up for the school's biggest nerd, Emmit (David Dorfman), the three of them signed up for hourly beat-downs by the high school bullies. The two bullies, Filkins (Alex Frost) and Ronnie (Josh Peck), who look like they should be in college, make it their mission to give these three kids the full high school experience. After visiting the principal and realizing that no one can protect them, they resort to hiring a low-budget bodyguard, Drillbit Taylor (Owen Wilson). Drillbit assures them that he is watching over them, even when he is nowhere in sight.Meanwhile, Drillbit is living in a tent off the side of the road and knows absolutely nothing about protecting the helpless kids. Although the film had its funny scenes, Wilson was basically sitting this one out. He gave an average performance compared to what he has done in the past. He had a few amusing lines that showed he is still that same old fun-loving guy, but with less passion and force. Steven Brill, who directed "Mr. Deeds," directed this film, which does not really say much. However, the film was co-written by Seth Rogen, who also co-wrote "Superbad." "Drillbit Taylor" is not nearly as risky and hilarious as Rogen's past projects. Ryan, the fat kid of the nerdy trio, practically made the movie. He had this undeniable don't-mess-with-me attitude, even though he had no intention of standing up to the bullies. He insisted on being called "T-Dog" and actually made Filkins look like a fool when they had a rap-off.The bully roles were well played, but it never made sense how the hallways just happened to be free of teachers every time the kids were getting picked on. While the kids in this film are played by unknowns, they clearly stole the show. Maybe Wilson can take a few pointers from them.


text:
My Favorite Crime Shows & Movies
5 Shows & Movies | by Ellen Wheeler
Jerry, a small-town Minnesota car salesman is bursting at the seams with debt... but he's got a plan. He's going to hire two thugs to kidnap his wife in a scheme to collect a hefty ransom from his wealthy father-in-law. It's going to be a snap and nobody's going to get hurt... until people start dying. Enter Police Chief Marge, a coffee-drinking, parka-wearing - and extremely pregnant - investigator who'll stop at nothing to get her man. And if you think her small-time investigative skills will give the crooks a run for their ransom... you betcha!
A troubled war veteran is assigned to protect a controversial politician who may be the target of a terror plot.
Dexter Morgan, a blood spatter pattern analyst for the Miami Metro Police also leads a secret life as a serial killer, hunting down criminals who have slipped through the cracks of justice.
Adrian Monk was once a rising star with the San Francisco Police Department, legendary for using unconventional means to solve the department's most baffling cases. But after the tragic (and still unsolved) murder of his wife Trudy, he developed an extreme case of obsessive-compulsive disorder. Now working as a private consultant, Monk continues to investigate cases in the most unconventional ways.


text:
Latest in Michaela Conlin
Michaela Conlin Joins 'Yellowstone'; 'Carnival Row' Casts Ariyon Bakare
Former Bones star Michaela Conlin is set for a recurring role in Yellowstone, Paramount Network's straight-to-series period drama set to premiere in 2018. Yellowstone follows the Dutton family, led by John Dutton (Costner), who controls the largest contiguous ranch in the United States. It's under constant attack by…
Michaela Conlin Joins Kate Beckinsale In 'The Disappointments Room'
EXCLUSIVE: Bones actress Michaela Conlin is the latest to enter Relativity's The Disappointments Room, the psychological thriller currently filming in North Carolina under director DJ Caruso. Wentworth Miller (Prison Break) scripted the Shining-esque ghost story "inspired by true events" about Dana (Beckinsale), who…
By Jen Yamato
'The Words' Helmer Brian Klugman Tackles Romantic Drama 'Baby, Baby, Baby'
EXCLUSIVE: Actor, writer, and director Brian Klugman sold his literary-themed directorial debut The Words to CBS Films out of Sundance. Now he's set his next drama about a writer navigating life and relationships, which he'll again work in front of and behind the camera. Baby, Baby, Baby tracks the relationship…


At the WWDC 2017, a new framework, Apple ARKit was introduced in iOS 11 to let the developers to create augmented reality experiences for iPad and iPhone via apps. Apple is expecting this framework to take its apps beyond the screen by allowing them to communicate with the real world in new ways completely. It offers Xcode app templates, Scale estimation, Ambient lighting estimation, Plane estimation with basic boundaries, stable motion tracking and much more.
With ARKit, iPhone and iPad can analyze the scene presented by the camera view and find horizontal planes in the room. ARKit can detect horizontal planes like tables and floors, and can track and place objects on smaller feature points as well.
ARKit runs on the Apple A9 and A10 processors. You can take advantage of the optimizations for ARKit in Metal, SceneKit, and third-party tools like Unity and Unreal Engine.


Why is Mozilla so hostile to its users ?
Why is it that one can loyally use a product and yet have the manufacturers hostile to your use ? Is Mozilla looking for money ? I would be glad to send money. My impression is that Mozilla simply is biding its time in order to super monetize the users. Prove to me I am wrong.


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text:
The Romania Journal - Daily News in English from Romania - Daily news in English from Romania
SCIENCE&CURIOSITIES
GOING OUT TIPS
MUSEUMS, ART GALLERIES & AUCTIONS
THEATER & OPERA
Romanian football champion's coach, Dan Petrescu, tests positive for COVID-19. Other 12…
Romania Journal	 Jul 22, 2020 0
Dan Petrescu, the coach of CFR Cluj, Romania's football champion, has tested positive for COVID-19, according to gsp.ro. He is the 13th member of the football club infected with the novel coronavirus. Dan Petrescu has been transported to…
Champions League: CFR Cluj defeats FC Astana 3-1 to qualify to second preliminary qualifying round
Valeriu Lazar	 Jul 18, 2019 0
Romania's football champions CFR Cluj has qualified to the second preliminary qualifying round of the Champions League Wednesday night, after defeating Kazakhstan's FC Astana 3-1 (2-1) in Cluj-Napoca. The Romanian champions have…
UEFA fines CFR Cluj EUR 200,000 for failure to comply with Financial Fair-Play regulations
Valeriu Lazar	 Jun 21, 2019 0
CFR Cluj, Romanian football champions, have been fined EUR 200,000 by the UEFA Club Financial Control Body (CFCB), for failing to comply with the Financial Fair-Play regulations, a release posted on the website and quoted by hotnews.ro…
Football: Gheorghe Hagi vs. Ladislau Boloni in EL – Cup winners FC Viitorul to play against…
FC Viitorul Constanta, the Romanian Cup winners, will face the Belgian team Royal Antwerp, coached by the former Romanian international Ladislau Boloni, in Europa League second qualifying round, according to the draw at Nyon on Wednesday.…
Europa League: FCSB, CFR Cluj eliminated in the play-off. No Romanian teams in the competition
Victor Lupu	 Aug 31, 2018 0
Romanian vice-champions FCSB were eliminated by Rapid Vienna on Thursday in the play-off of Europa League, although they won the match 2-1. In the first match, Rapid Vienna won 3-1 and FCSB managed only 201 in the return match on…
Europa League: CFR Cluj and FCSB defeated in the play-off first round
Romanian football champions CFR Cluj were defeated by Luxemburgish team F91 Dudelange 2-0 (0-0) Thursday evening in the first round of the Europa league play-off. The match was played on 'Josy Barthel' stadium in Luxembourg. The goals…
Europa League: FCSB, CFR Cluj through to play-off. Universitatea Craiova – out
Romanian Vice-champions DCSB have qualified to the Europa League play-off Thursday evening, after defeating Hajduk Split 2-1 on National Arena in Bucharest. In the first round the score was 0-0. The victory was achieved in extra-time, by…
Europa League: FCSB, CFR Cluj and Universitatea Craiova will play in the third qualifying round
Victor Lupu	 Aug 3, 2018 0
FCSB, CFR Cluj and Universitatea Craiova are the Romanian teams to play in the third qualifying round of Europa League. FCSR defeated on Thursday Rudar Velenje 3-0, after the 2-0 win away a week ago, and will face Hajduk Split (Croatia),…
CFR Cluj fails to qualify to the CL third qualifying round after 1-1 draw with Malmo FF
Romanian football champions CFR Cluj concluded in a draw 1-1 the match on Wednesday against Swedish team, Malmo FF, the in second Champions League qualifying round, failing thus to qualify to the next stage. CFR Cluj led 1-0 in the first…
CFR Cluj 0 Malmo 1 in Champions League second qualifying round
Victor Lupu	 Jul 25, 2018 0
Romanian football champions CFR Cluj played a disappointing match Tuesday evening, as it was defeated by Malmo 0-1, the hosts had no shots on target. The match counted for the second qualifying round of Champions League. The team has…
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ECHR sentences Romania for violating the right to…
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Allianz Risk Barometer 2021: Covid-19 trio tops global…
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text:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace EnumerableTest.Sandbox
{
    public class Class3
    {
        public IEnumerable<Test> test_success()
        {
            yield return 0.Is(0);
        }
    }
}


text:
Q: Replace line breaks in the code but still keep line breaks in textarea Right now, I'm using a CMS that displays the content of a textarea in a "Settings" page, on another page, but the content is copied inside some jQuery code:
$('textarea#envoi_contrat_conf').val('<cms:pages masterpage="gabarits.php" page_name="envoi-contrat-conferencier"><cms:show gabarit_content/></cms:pages>');

The content between the <cms:pages></cms:pages> tags is what is being saved in that textarea on the "Settings" page.
But right now, if I enter this in the textarea of the Settings page :
Hello
This is
A test

It will be displayed the same way, keeping the line breaks in the jQuery code. So it will break it. My code would look like this:
    $('textarea#envoi_contrat_conf').val('Hello
This is
A test');

What I need, is to make sure everything that is going to be inserted in that "val" section, replaces line breaks with something that will still keep the line breaks once being copied in the textarea.
If something like this would work, I would be fine with is:
    $('textarea#envoi_contrat_conf').val('Hello
This is
A test');

But
 doesn't work inside a textarea.
Any ideas?
Thanks a lot!

Someone linked another topic that was not related to my question.
Another user pointed out that I was looking for "\n" instead of actual line breaks. Thanks for his help.
But now I need to know how to replace actual line breaks in the code by "\n" instead. Since it's CMS generated content, I cannot hardcode a sentence. So I need to make sure even if the user makes line breaks in the textarea content, that it will appear with "\n" in the jQuery code instead of real line breaks in the code.
I tried this:
$('textarea#envoi_contrat_conf').val().replace(/\r\n|\r|\n/g,'\n');

Which successfully allow me to replace line breaks with something else, but the \n to be printed as is, not rendered. Thanks

A: Solution

var str = `${$('textarea#envoi_contrat_conf').val()}`

Template Literals
Not sure where or how you are getting a string with multiple line breaks but there is a way you can use such a string in the code without modifying it anywhere along the process.

*

*However you got this string it looks like this:
"first segment
second segment
third segment"

*Store the string in a variable and interpolate it as a Template Literal by wrapping the variable in `${...}`
`
$
{
multiline string in variable or as a return from a function
}
`
Note: The grave accent key (a.k.a. backtick): ` is located at the top left corner of every QWERTY keyboard (usually below the esc key.)

That's the how, now the why. Literal Strings are representation of a string wrapped in single or double quotes and are a syncytial nightmare. Template Literals are representation of a string wrapped in grave accents and have syntactic sugar that rectifies the problems the syntax of Literal strings cause. One important improvement is that a break in a string does not break it's value.
`text line 1
 text line 2`;

"text line 1\n"+
"text line 2";

So wrapping a string value in backticks makes it a Template Literal. The ${... } combo interpolates a value or expression into the TL. Compare:
Template Literal Interpolation
`Template Literal interpolation: ${X} is easier to read and write, and " and ' are just normal characters`;

Literal String Concatenation
"Literal String concatenation: '+X+' is harder to read and write, and \" and \' have equal meaning so they must be escaped or used in nested combinations."

Demo

var str = `${$('.text0').val()}`;

$('.text1').val(str);
<textarea class='text0' rows='5'>
Hello
This is
A test
</textarea>

<textarea class='text1' rows='5'></textarea>

A: Got it!
Here's the code I used:
$('textarea#envoi_contrat_conf').val().replace(/\r\n|\r|\n/g,"\\n");

The extra backslash espaces the first one, which needs to be printed out.
Thanks for Tibrogargan for the help

redpajama_set_name: RedPajamaStackExchange


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text:
D-Sat is a three-unit CubeSat designed, built, and operated by D-Orbit. It will be launched into a 500 km sun-synchronous orbit.
D-Sat was launched on June 23rd, 2017 with the goal to validate our solid propellant motor in Space.
Moreover, as we also wanted to make sure that this mission would harness the full potential and opportunities granted by sending a satellite to space, we partnered with several institutions to include three experiments that will be performed throughout the mission's lifetime. In tune with our philosophy, all three experiments have a social impact.
To understand the importance of this mission, first we need to address the problem that makes it relevant: space debris.
The problem: space debris
Our world is increasingly dependent on space-based assets for applications like Earth observation, weather forecast, global navigation, disaster prevention, high-precision farming, and self-driving cars.
This technological progress is leaving Earth's orbit crowded by an increasing population of man-made objects that no longer serve a useful purpose, like nonfunctioning satellites, rocket's upper stages, and other objects released during a space mission. A collision between two larger pieces of debris and the explosion of rocket bodies are the kind of events that can create further smaller debris. According to NASA, there are hundreds of thousands of pieces of debris ranging in size between 1 cm (0,4 inches) and 10 cm (4 inches) traveling around Earth, and this number is likely to increase if we keep leaving nonfunctioning satellites in orbit.
Blog post: Space junk... demystified
Blog post: CURIOSITY FEATURE - Entertainment Edition: From Gravity to our mission
Infographic: Let's talk trash
OUR SOLUTION: SElf-decommissioning satellites
While there is not much we can do for space junk that is already in orbit, we realized that things could quickly improve by following a simple principle: everything that goes up must come down as soon as it is not useful.
To make this possible we imagined a device with three key characteristics. First of all, it needed to be compact and lightweight, easy to integrate it inside existing satellite platforms during design and manufacturing. Second, it had to be able to execute a precise direct re-entry maneuver at the end of the satellite's mission. Finally, it had to be independent from the main spacecraft's systems, so it would work even in case of major malfunction of the hosting satellite.
After months on the drawing board, we came up with D-Orbit Decommissioning Device (D3), a smart propulsive system that can be scaled to easily fit any size spacecraft. Its compact solid rocket motor is designed to execute a high-trust burn to quickly remove a spacecraft form orbit. Its independent control unit and communication system ensures that the system can be activated and controlled from the ground even if the spacecraft has ceased operations.
Blog post: Satellite decommissioning in LEO: how to do it properly
the experiments
Other than technology validation, the other main goal of the D-Sat mission is scientific investigation.
In fact, throughout its mission D-Sat will perform three experiments: SatAlert, Debris Collision Alerting System (DeCas), and Atmosphere Analyzer.
SatAlert, designed in collaboration with the National Inter-University Consortium for Telecommunications (CNIT), University of Florence Research Unit, is an in-orbit validation of the Multiple Alert Message Encapsulation (MAMES) protocol, defined by ETSI (European Telecommunications Standards Institute). MAMES is an extensible multiple alert message encapsulation protocol for transporting alert messages of different formats over satellite links. It guarantees the timely distribution of alert messages to the people affected by critical situations in context with limited network coverage.
During the experiment D-Sat will collect MAMES emergency messages sent from a ground station, store them onboard, and re-broadcast them to national public safety entities upon receiving a trigger command. This experiment will validate a typical emergency scenario where civil defense agencies need a means to broadcast instructions in areas affected by natural disasters when the ground telecommunication infrastructures have been damaged.
These emergency scenarios are becoming increasingly common, given the tight correlation between climate change and extreme weather conditions like hurricanes, wildfires, tornadoes, extreme rainfalls, and floods, and for this reason SatAlert may help saving thousands of lives.
SCIENTIFIC AMERICAN: Is There a Strong Link between Extreme Weather Events and Climate Change?
TIME: Scientists Are Making Stronger Links Between Climate Change and Extreme Weather
NY TIMES: Scientists Study Links Between Climate Change and Extreme Weather
EPA: Understanding the Link Between Climate Change and Extreme Weather
DeCAS (Debris Collision Alerting System), developed by Aviosonic Space Tech, is a patented system able to determine the dynamics of the debris footprint area associated to the re-entry of the hosting satellite During re-entry, DeCAS activates itself, determines its own position and broadcasts its location and the debris footprint dynamics forecast to civil protection agencies. In a real-world scenario, this information would be processed on ground and then transmitted in real-time to airplanes flying over that zone through the Air Traffic Control Center, and to the populated areas below through national public safety agencies.
During the in-orbit validation phase, the forecast of D-SAT simulated re-entry footprint will be encapsulated inside MAMES messages that will be transmitted to Aviosonic's ground station at Andrea Ponti Institute in Gallarate, Italy, validating all the DeCAS communication phases and the three segment DeCAS system (space, ground, and user).
During re-entry phase, DeCAS will perform a final test in operational conditions, demonstrating the viability of the technology.
Atmosphere Analyzer is a data collection experiment aimed at collecting in-situ atmospheric data from the lower ionosphere during the re-entry maneuver. Before breaking up, D-Sat will cross the region between 80 km and 150 km. This is the least studied region of the atmosphere because it can't be reached by satellites or stratospheric balloons. By doing so, D-SAT will validate an innovative approach for an in-situ data collection system that will be further developed in a dedicated mission in 2019.
Infographic: Mission timeline


text:
Pete Buttigieg says he won't be the first gay president if elected
By Chris Perez
June 16, 2019 | 9:02pm | Updated June 17, 2019 | 9:29am
Pete Buttigieg Getty Images
Gay men are signing up for Hinge in droves because of Pete Buttigieg
Pete Buttigieg echoes JFK with service plan
Sanders scores $18M in donations, but lags behind Buttigieg
Pete Buttigieg raises nearly $25 million in second quarter
Mayor Pete Buttigieg doesn't believe he'll be the first gay president if elected in 2020.
"I would imagine we've probably had excellent presidents who were gay — we just didn't know which ones," he told "Axios on HBO."
"I mean, statistically, it's almost certain."
Asked if he possibly knew which commander-in-chief wasn't straight, the Democratic hopeful said: "My gaydar even doesn't work that well in the present, let alone retroactively. But one can only assume that's the case."
Buttigieg — who is mayor of South Bend, Ind. — has been rising in the polls as of late. He would be the first openly gay presidential candidate, if nominated next year.
The 37-year-old has been asked in the past about the possibility of there ever being a gay president, with BuzzFeed posing the question back in March.
"My gaydar is not great to begin with and definitely doesn't work over long stretches of time," he repeated. "I think we'll just have to let the historians figure that out."
Filed under 2020 presidential election , democrats , Pete Buttigieg
Trump dumps pollsters after results show Biden victory


text:
Q: How does coassociativity of a coalgebra $C$ imply that the derivation on $\Omega C$ is a differential? I am trying to show that $d²=0$ where $d$ is the derivation on $T(s^{-1}\bar{C})$ induced by the map $s^{-1}\bar{C}\to T(s^{-1}\bar{C})$ defined by
$$s^{-1}x\mapsto -\sum (-1)^{|x_{(1)}|}s^{-1}x_{(1)}\otimes s^{-1}x_{(2)}$$ where
$\bar{\Delta}(x)=\sum x_{(1)}\otimes x_{(2)}$.
I tried doing this by considering an element $s^{-1}c_1\otimes...\otimes s^{-1}c_k\in T(s^{-1}\bar{C})$. Because $d$ is linear and the elemets of $T(s^{-1}\bar{C})$ are sums of elements of this form this should suffice.
Leibniz rule tells us that
$$d(s^{-1}c_1\otimes...\otimes s^{-1}c_k)=\\
(\sum 1\otimes...\otimes 1 \otimes d\otimes 1\otimes ...\otimes 1)(s^{-1}c_1\otimes...\otimes s^{-1}c_k).$$
By writing out the formulas and keeping track of all the signs I managed to prove that when we apply $d$ twice, the term we get from first applying $d$ on the $i$'th position and then applying $d$ on the $j$'th position cancels with the term we get from first applying $d$ on the $j$'th position and then applying $d$ on the $i$'th position as long as $j\neq i,i+ 1$. However, I got stuck with the terms we get from first applying $d$ on the $i$'th position and then applying $d$ on the $(i+1)$'th position. I know coassociativity of $C$ should imply that these terms cancel with the terms we get by applying $d$ on the $i$'th position twice but I cannot prove it.
In the book Algebraic Operads by Loday and Vallette they write out a proof of it (page 44) but I do not understand it. They only consider $d^2$ applied to an element of the form $s^{-1}x$. Then I think that they identify terms of
$$(\bar{\Delta}\otimes 1)\circ \bar{\Delta}(x):=\sum x_{(1)}\otimes x_{(2)}\otimes x_{(3)}$$
with terms of
$$(1\otimes \bar{\Delta})\circ \bar{\Delta}(x):=\sum x_{(1)}'\otimes x_{(2)}'\otimes x_{(3)}'$$ and show how they cancel given the signs they get in $d²$. This is what I don't understand because coassociativity of $C$ only tells us $(\bar{\Delta}\otimes 1)\circ \bar{\Delta}(x)=(1\otimes \bar{\Delta})\circ \bar{\Delta}(x)$ but $$(\bar{\Delta}\otimes 1)\circ \bar{\Delta}(x)=\sum x_{(1)}\otimes x_{(2)}\otimes x_{(3)}$$ need not contain the same terms as $$(1\otimes \bar{\Delta})\circ \bar{\Delta}(x)=\sum x_{(1)}'\otimes x_{(2)}'\otimes x_{(3)}'.$$
Maybe someone who is familiar with the book and/or the theory can help bring clarity here=)

A: You are missing many signs from desuspension, which you did write in your first sentence, but didn't afterwards.
First, recall that since the differentail, call it $d$, induced on $\Omega C$, obtained from the comultiplication, is a derivation, and since $\Omega C$ is generated in degree $1$ by $V=s^{-1}\overline C$, it suffices you prove the claim on these generators. Now the differential is given as follows: take $s^{-1}c$ a generator, desuspend, and comultiply to get $c_{(1)}\otimes c_{(2)}$, and then multiply by $s^{-1}\otimes s^{-1}$ to get (from the Koszul rule and the interchange of $c_{(1)}$ and $s^{-1}$)
$$d(s^{-1}c) = -(-1)^{|c_{(1)}|}s^{-1}c_{(1)}\otimes s^{-1}c_{(2)}.$$
If you apply $d$ again, you will get two terms, one is
$$ (-1)^{|c_{(1)}|+|c_{(1)(1)}|}s^{-1}c_{(1)(1)}\otimes s^{-1}c_{(1)(2)}\otimes s^{-1}c_{(2)},$$
where the sign $|c_{(1)(1)}|$ comes from the same rule as above. The other is
$$ (-1)^{|c_{(1)}|+|c_{(2)(1)}|+|c_{(1)}|-1}s^{-1}c_{(1)}\otimes s^{-1}c_{(2)(1)}\otimes s^{-1}c_{(2)(2)},$$
where $|c_{(1)}|-1$ comes from $d$ going over $s^{-1}c_{(1)}$, so the overall sign is just $|c_{(2)(1)}|-1$.
To get the global sign of the first term, note that $|c_{(1)}|= |c_{(1)(1)}| +|c_{(1)(2)}|$, so the sign is $|c_{(1)(2)}|$. Writing everything in Sweedler notation, you get
$$(-1)^{|c_{(2)}|}c_{(1)}\otimes c_{(2)}\otimes c_{(3)} + (-1)^{|c_{(2)}|-1}c_{(1)}\otimes c_{(2)}\otimes c_{(3)} = 0$$


text:
Elitserien i handboll för damer 2005/2006 spelades 15 september 2005-18 mars 2006 och vanns av Skövde HF. IK Sävehof vann dock det svenska mästerskapet efter slutspel.

Sluttabell

Slutspel om svenska mästerskapet

Kvartsfinaler: bäst av tre
25 mars 2006: Team Eslövs IK-Spårvägens HF 22-26
25 mars 2006: Skånela IF-IK Sävehof 26-27 efter förlängning
26 mars 2006: Skuru IK-Önnereds HK 24-22
26 mars 2006: Irsta Västerås-Skövde HF 23-21

29 mars 2006: Skövde HF-Irsta Västerås 18-17
29 mars 2006: Spårvägens HF-Team Eslövs IK 24-20 (Spårvägens HF vidare med 2-0 i matcher)
29 mars 2006: Önnereds HK-Skuru IK 22-24 (Önnereds HK vidare med 2-0 i matcher)
30 mars 2006: IK Sävehof-Skånela IF 29-22 (IK Sävehof vidare med 2-0 i matcher)

1 april 2006: Skövde HF-Irsta Västerås 34-23 (Skövde HF vidare med 2-1 i matcher)

Semifinaler: bäst av fem
10 april 2006: IK Sävehof-Spårvägens HF 23-21
12 april 2006: Skövde HF-Skuru IK 27-25 efter förlängning

19 april 2006: Spårvägens HF-IK Sävehof 20-26
20 april 2006: Skuru IK-Skövde HF 22-16

22 april 2006: Skövde HF-Skuru IK 32-16
23 april 2006: IK Sävehof-Spårvägens HF 26-15 (IK Sävehof vidare med 3-0 i matcher)

26 april 2006: Skuru IK-Skövde HF (Skövde HF vidare med 3-1 i matcher)

Final
6 maj 2006: Skövde HF-IK Sävehof 21-22 (Scandinavium)

IK Sävehof svenska mästarinnor säsongen 2005/2006.

Skytteligan
Charlotte Brandt, Kungälvs HK - 22 matcher, 139 mål

Källor

Fotnoter

Externa länkar

 Everysport - Elitserien i handboll för damer 2005/2006

Sport i Sverige 2005
Sport i Sverige 2006
Handbollssäsongen 2005/2006
2005/2006

redpajama_set_name: RedPajamaWikipedia


It has long been a curatorial fantasy of mine to organise a re-staging of a boxing match that took place in 1972 between the German artist Joseph Beuys, regarded as a giant of modern art, with a local art student of his.
But the EU is a very modern empire which extends its power through regulation and bureaucracy not with armies.
Read the postDown With All Empires!
Decolonising the Curriculum: Whose Knowledge is it Anyway?
We need a debate about the curriculum and the knowledge we teach pupils in schools and later on at university level.
The decolonising the curriculum movement isn't it.
Read the postDecolonising the Curriculum: Whose Knowledge is it Anyway?


In all honesty, the unlawful fixing inside your joint, bong or space-cake does not include profoundly on the rundown of perilous medications. However, regardless of whether you are worried about your psychological or physical wellbeing, the budgetary cost, the drudgery of carrying on with your life stoned or in light of the fact that you need to remove yourself from your present way of life, your motivations to stop are as sound as any. Apparently, around 500 cannabis smokers in the UK search out treatment consistently. That measurement demonstrates two vital things: a) cannabis genuinely can possibly demolish lives, and b) you are not the only one in your craving to stop.
Whenever cocaine and heroin addicts at long last wound up in a sorry situation they have every one of the reasons on the planet to stop. With cannabis the decay is moderate and scarcely discernible. Its crush upon our lives is apparently extremely delicate. So delicate in actuality we become acclimated to feeling lazy and stoned and overlook what it resembles to get up in the mornings overflowing with vitality and energy. Rather, laziness, blushed eyes and depleted skin tone end up ordinary. Notwithstanding when it ends up evident we ought to at any rate think about halting, the advantages dependably appear to be weighted for proceeding.
Such is the nuance of cannabis a great many people experience issues choosing if cannabis is their adversary or partner. A similar medication it shows up can calm upsetting circumstances and cause distressing circumstances by method for nervousness and suspicion. Similarly, cannabis seems, by all accounts, to be a relaxant yet it's normal to encounter contemplative self-uncertainty and need trust in social circumstances.
It's the nuance of cannabis that additionally makes the disarray about whether we are basically in the propensity for smoking excessively or mentally dependent on it. Whatever reality of the circumstance is, it's very simple to walk through your teenagers, thirties for all time stoned; yet feel terminally exhausted and denied of fun and unwinding without it.


text:
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There's nothing shy about California and its alluring charms are well and truly on display. Hollywood, Disneyland, Zuma Beach, the state of California boasts a huge number of stunning tourist draws, both natural and manmade, that bring in visitors from all over the world. But what about the lesser-known sites all over California where you can experience the true beauty of the golden state.
From the arid, other-worldly sand dunes of Death Valley and the pre-historic ferns of Fern Canyon to the quaint Danish charm of small-town Solvang and the golden bling of Coronado Beach, California has lots to offer the beauty-hunting explorer. Taking care to look for places off the beaten path and mix them in with never-to-be-missed staples of the Californian tourist's diet, we've put together a list of the 11 most beautiful places in California. Enjoy.
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14 Most Beautiful Places in Northern America
9 Best Ski Destinations in New York
Top 7 Shopping Outlets in The United States
Looking for some deals on holiday shopping? Check out these best outlet malls spread across the United States. You'll be sure to find discounts at these stores, saving you more money in your pockets! Orlando...
The Best Luggage for 2023
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Places To Travel Celebrating The New Year
Venice, Italy If you're looking for a romantic destination for New Year's Eve, visit Venice, Italy. Love and passion for a beautiful city is all you'll need. Celebrate the night away with plenty to do in Italy's top...
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Озі́рна — село в Україні, у Звенигородському районі Черкаської області, центр сільської ради. Населення — 1200 чоловік, 645 дворів (на 1 січня 2008 року).

Село розташоване за 5 км на північний захід від районного центру — міста Звенигородка та за 23 км від залізничної станції Звенигородка.. Через село проходить автошлях Черкаси — Умань та вузькоколійна залізниця Звенигородка — Бужанка.

Історія
Село відоме з XVII століття. За переказами, назва села походить від назви місця, де стояла сторожова вежа з вартою. Це були дозорці, які постійно озирали місцевість, а при появі ворогів сповіщали людей про небезпеку. Таким чином, Озірна — це місце, з якого озирають прилеглі землі. За другою версією, назва походить від 13 озер в урочищі Озірна. Ще за однією версією, назва походить від прізвища першого поселенця Тимура Озірного.

У 1778 році в селі побудовано дерев'яну Свято-Покровську церкву. У 1830 році тут почала діяти однокласна церковно-парафіяльна школа з дворічним терміном навчання. З 1887 року запроваджено чотирирічний термін навчання російською мовою.

З 1900 року у селі працював фельдшер. У часи столипінської реформи в селі проживало понад 2 000 осіб, тут виникло кілька нових хуторів.

У 1917 році створено сільську раду, від якої кілька депутатів було в Неморозькій волості. З липня 1920 року в селі діяв комітет незаможних селян. У 1924 році було відкрито сільський клуб, три магазини.

У 1929 році, під час примусової колективізації, у селі створено три товариства спільного обробітку землі: «Крок вперед», ім. Шевченка та ім. Петровського, — які в 1930 році об'єдналися у колгосп ім. Петровського. У 1932 році створено машинно-тракторну станцію. З 30-х по 70-ті роки повз село проходила вузькоколійна залізниця, яка сполучала станцію Звенигородка і село Бужанка.

Село постраждало внаслідок геноциду українського народу, проведеного окупаційним урядом СССР 1932—1933 та 1946–1947 роках.

У часи голодомору 1932—1933 років померло близько 1000 жителів села. У 1991 році в селі встановлено пам'ятний знак жертвам цієї всенародної трагедії.

411 мешканців села брали участь у боях радянсько-німецької війни, 126 з них загинули, 71 нагороджені орденами й медалями. В селі споруджено пам'ятник і обеліск Слави воїнам, що загинули в боях за відвоювання села.

У 1965 році на базі колгоспів ім. Петровського та «Прогрес» (Неморож) створено колгосп ім. Мічуріна з центральною садибою в Озірній. За ним було закріплено 4,8 тисяч га сільськогосподарських угідь, у тому числі 4 тисячі га орної землі. Господарство вирощувало зернові культури і цукрові буряки. Допоміжними галузями були — городництво, садівництво, рибництво, бджільництво.

На території села знаходились відділення Звенигородського районного об'єднання «Сільгосптехніки», державна племінна станція штучного запліднення тварин, метеорологічна станція.

Також на той час працювали восьмирічна школа, клуб, бібліотека з фондом 5 тисяч книг, фельдшерсько-акушерський пункт, майстерня пошиття одягу.

Сучасність
На сьогодні на території села функціонують ЗОШ І-ІІ ступенів, фельдшерсько-акушерський пункт, бібліотека, відділення зв'язку, 4 магазини; діють метеорологічна станція, ТОВ «Звенигородське племпідприємство», ПП «Урос», ПП «Сервіс-лан», СТОВ МТС «Морал», Озірнянська філія Кам'янець-Подільський Комбікормовий завод ТОВ "СП"ВЕСНА-21", ТОВ «Агропроменерго», ТОВ НВФ «Урожай», 6 фермерських господарств. У селі відроджено Свято-Покровську церкву.
Відновлено Дитячий Садок «Сонечко»

Відомі люди

У селі народилися:
 Марцюк Зиновій (нар. в жовтні 1910 р.) — головний фінансист ОУН, фінансовий референт ОУН, організатор збірок на фонд УПА.

 Микола Петрович Степаненко (31.01.1972 — 18.03.2022) — штаб-сержант Збройних Сил України, учасник російсько-української війни, що загинув під час російського вторгнення в Україну

Див. також
 Перелік населених пунктів, що постраждали від Голодомору 1932—1933 (Черкаська область)

Джерела

Посилання

http://stupaem.at.ua/ сайт села

Література

Ресурси інтернету
 who-is-who.com.ua

Населені пункти, засновані у 17 столітті
Села Черкаської області