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    <titleInfo>
        <title>Citation Sentence Generation Leveraging the Content of Cited Papers</title>
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        <namePart type="given">Akito</namePart>
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    <name type="personal">
        <namePart type="given">Hiroaki</namePart>
        <namePart type="family">Sugiyama</namePart>
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    <name type="personal">
        <namePart type="given">Kohji</namePart>
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    <name type="personal">
        <namePart type="given">Rikuto</namePart>
        <namePart type="family">Tanaka</namePart>
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    <name type="personal">
        <namePart type="given">Hirotoshi</namePart>
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    <abstract>We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited. Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences. Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.</abstract>
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        <url>https://aclanthology.org/2022.sdp-1.19</url>
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        <date>2022-10</date>
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%0 Conference Proceedings
%T Citation Sentence Generation Leveraging the Content of Cited Papers
%A Arita, Akito
%A Sugiyama, Hiroaki
%A Dohsaka, Kohji
%A Tanaka, Rikuto
%A Taira, Hirotoshi
%S Proceedings of the Third Workshop on Scholarly Document Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F arita-etal-2022-citation
%X We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited. Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences. Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.
%U https://aclanthology.org/2022.sdp-1.19
%P 170-174

Markdown (Informal)

[Citation Sentence Generation Leveraging the Content of Cited Papers](https://aclanthology.org/2022.sdp-1.19) (Arita et al., sdp 2022)

• Citation Sentence Generation Leveraging the Content of Cited Papers (Arita et al., sdp 2022)

ACL

• Akito Arita, Hiroaki Sugiyama, Kohji Dohsaka, Rikuto Tanaka, and Hirotoshi Taira. 2022. Citation Sentence Generation Leveraging the Content of Cited Papers. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 170–174, Gyeongju, Republic of Korea. Association for Computational Linguistics.