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        <title>TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks</title>
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        <namePart type="given">Guy</namePart>
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    <name type="personal">
        <namePart type="given">Michal</namePart>
        <namePart type="family">Shmueli-Scheuer</namePart>
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    <name type="personal">
        <namePart type="given">Jonathan</namePart>
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    <name type="personal">
        <namePart type="given">Achiya</namePart>
        <namePart type="family">Jerbi</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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            <namePart type="given">Lluís</namePart>
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    <abstract>Currently, no large-scale training data is available for the task of scientific paper summarization. In this paper, we propose a novel method that automatically generates summaries for scientific papers, by utilizing videos of talks at scientific conferences. We hypothesize that such talks constitute a coherent and concise description of the papers’ content, and can form the basis for good summaries. We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries. A model trained on this dataset achieves similar performance as models trained on a dataset of summaries created manually. In addition, we validated the quality of our summaries by human experts.</abstract>
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%0 Conference Proceedings
%T TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
%A Lev, Guy
%A Shmueli-Scheuer, Michal
%A Herzig, Jonathan
%A Jerbi, Achiya
%A Konopnicki, David
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F lev-etal-2019-talksumm
%X Currently, no large-scale training data is available for the task of scientific paper summarization. In this paper, we propose a novel method that automatically generates summaries for scientific papers, by utilizing videos of talks at scientific conferences. We hypothesize that such talks constitute a coherent and concise description of the papers’ content, and can form the basis for good summaries. We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries. A model trained on this dataset achieves similar performance as models trained on a dataset of summaries created manually. In addition, we validated the quality of our summaries by human experts.
%R 10.18653/v1/P19-1204
%U https://aclanthology.org/P19-1204
%U https://doi.org/10.18653/v1/P19-1204
%P 2125-2131

Markdown (Informal)

[TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks](https://aclanthology.org/P19-1204) (Lev et al., ACL 2019)

• TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks (Lev et al., ACL 2019)

ACL

• Guy Lev, Michal Shmueli-Scheuer, Jonathan Herzig, Achiya Jerbi, and David Konopnicki. 2019. TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2125–2131, Florence, Italy. Association for Computational Linguistics.