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        <title>Citance-Contextualized Summarization of Scientific Papers</title>
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        <namePart type="given">Shahbaz</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Martin</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
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        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Current approaches to automatic summarization of scientific papers generate informative summaries in the form of abstracts. However, abstracts are not intended to show the relationship between a paper and the references cited in it. We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called “citance”). This summary outlines content of the cited paper relevant to the citation location. Thus, our approach extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. We evaluate our approach using **Webis-Context-SciSumm-2023**, a new dataset containing 540K computer science papers and 4.6M citances therein.</abstract>
    <identifier type="citekey">syed-etal-2023-citance</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.573</identifier>
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        <date>2023-12</date>
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            <start>8551</start>
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%0 Conference Proceedings
%T Citance-Contextualized Summarization of Scientific Papers
%A Syed, Shahbaz
%A Hakimi, Ahmad
%A Al-Khatib, Khalid
%A Potthast, Martin
%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 syed-etal-2023-citance
%X Current approaches to automatic summarization of scientific papers generate informative summaries in the form of abstracts. However, abstracts are not intended to show the relationship between a paper and the references cited in it. We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called “citance”). This summary outlines content of the cited paper relevant to the citation location. Thus, our approach extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. We evaluate our approach using **Webis-Context-SciSumm-2023**, a new dataset containing 540K computer science papers and 4.6M citances therein.
%R 10.18653/v1/2023.findings-emnlp.573
%U https://aclanthology.org/2023.findings-emnlp.573
%U https://doi.org/10.18653/v1/2023.findings-emnlp.573
%P 8551-8568

Markdown (Informal)

[Citance-Contextualized Summarization of Scientific Papers](https://aclanthology.org/2023.findings-emnlp.573) (Syed et al., Findings 2023)

• Citance-Contextualized Summarization of Scientific Papers (Syed et al., Findings 2023)

ACL

• Shahbaz Syed, Ahmad Hakimi, Khalid Al-Khatib, and Martin Potthast. 2023. Citance-Contextualized Summarization of Scientific Papers. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8551–8568, Singapore. Association for Computational Linguistics.