<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yin-van-schijndel-2023-linguistic">
    <titleInfo>
        <title>Linguistic Compression in Single-Sentence Human-Written Summaries</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Fangcong</namePart>
        <namePart type="family">Yin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marten</namePart>
        <namePart type="family">van Schijndel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Summarizing texts involves significant cognitive efforts to compress information. While advances in automatic summarization systems have drawn attention from the NLP and linguistics communities to this topic, there is a lack of computational studies of linguistic patterns in human-written summaries. This work presents a large-scale corpus study of human-written single-sentence summaries. We analyzed the linguistic compression patterns from source documents to summaries at different granularities, and we found that summaries are generally written with morphological expansion, increased lexical diversity, and similar positional arrangements of specific words compared to the source across different genres. We also studied how linguistic compressions of different factors affect reader judgments of quality through a human study, with the results showing that the use of morphological and syntactic changes by summary writers matches reader preferences while lexical diversity and word specificity preferences are not aligned between summary writers and readers.</abstract>
    <identifier type="citekey">yin-van-schijndel-2023-linguistic</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.532</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.532</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>7922</start>
            <end>7935</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Linguistic Compression in Single-Sentence Human-Written Summaries
%A Yin, Fangcong
%A van Schijndel, Marten
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yin-van-schijndel-2023-linguistic
%X Summarizing texts involves significant cognitive efforts to compress information. While advances in automatic summarization systems have drawn attention from the NLP and linguistics communities to this topic, there is a lack of computational studies of linguistic patterns in human-written summaries. This work presents a large-scale corpus study of human-written single-sentence summaries. We analyzed the linguistic compression patterns from source documents to summaries at different granularities, and we found that summaries are generally written with morphological expansion, increased lexical diversity, and similar positional arrangements of specific words compared to the source across different genres. We also studied how linguistic compressions of different factors affect reader judgments of quality through a human study, with the results showing that the use of morphological and syntactic changes by summary writers matches reader preferences while lexical diversity and word specificity preferences are not aligned between summary writers and readers.
%R 10.18653/v1/2023.findings-emnlp.532
%U https://aclanthology.org/2023.findings-emnlp.532
%U https://doi.org/10.18653/v1/2023.findings-emnlp.532
%P 7922-7935

Markdown (Informal)

[Linguistic Compression in Single-Sentence Human-Written Summaries](https://aclanthology.org/2023.findings-emnlp.532) (Yin & van Schijndel, Findings 2023)

• Linguistic Compression in Single-Sentence Human-Written Summaries (Yin & van Schijndel, Findings 2023)

ACL

• Fangcong Yin and Marten van Schijndel. 2023. Linguistic Compression in Single-Sentence Human-Written Summaries. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7922–7935, Singapore. Association for Computational Linguistics.