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SciBERTSUM: Extractive Summarization for Scientific Documents

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Document Analysis Systems (DAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13237))

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Abstract

The summarization literature focuses on the summarization of news articles. The news articles in the CNN-DailyMail are relatively short documents with about 30 sentences per document on average. We introduce SciBERTSUM, our summarization framework designed for the summarization of long documents like scientific papers with more than 500 sentences. SciBERTSUM extends BERTSUM to long documents by 1) adding a section embedding layer to include section information in the sentence vector and 2) applying a sparse attention mechanism where each sentences will attend locally to nearby sentences and only a small number of sentences attend globally to all other sentences. We used slides generated by the authors of scientific papers as reference summaries since they contain the technical details from the paper. The results show the superiority of our model in terms of ROUGE scores. (The code is available at https://github.com/atharsefid/SciBERTSUM).

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Acknowledgement

Partial support from the National Science Foundation is gratefully acknowledged.

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Authors and Affiliations

  1. Computer Science and Engineering, Pennsylvania State University, University Park, PA, 16802, USA

    Athar Sefid & C. Lee Giles

  2. Information Sciences and Technology Department, Pennsylvania State University, University Park, PA, 16802, USA

    C. Lee Giles

Authors
  1. Athar Sefid
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  2. C. Lee Giles
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Correspondence to Athar Sefid .

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Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Seiichi Uchida

  2. Boise State University, BOISE, ID, USA

    Elisa Barney

  3. LIRIS UMR CNRS, Villeurbanne, France

    Véronique Eglin

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Sefid, A., Giles, C.L. (2022). SciBERTSUM: Extractive Summarization for Scientific Documents. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_46

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  • DOI: https://doi.org/10.1007/978-3-031-06555-2_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06554-5

  • Online ISBN: 978-3-031-06555-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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