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Domain-Driven and Discourse-Guided Scientific Summarisation

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

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Abstract

Scientific articles tend to follow a standardised discourse that enables a reader to quickly identify and extract useful or important information. We hypothesise that such structural conventions are strongly influenced by the scientific domain (e.g., Computer Science, Chemistry, etc.) and explore this through a novel extractive algorithm that utilises domain-specific discourse information for the task of abstract generation. In addition to being both simple and lightweight, the proposed algorithm constructs summaries in a structured and interpretable manner. In spite of these factors, we show that our approach outperforms strong baselines on the arXiv scientific summarisation dataset in both automatic and human evaluations, confirming that a scientific article’s domain strongly influences its discourse structure and can be leveraged to effectively improve its summarisation. Our code can be found at: https://github.com/TGoldsack1/DodoRank.

T. Goldsack and Z. Zhang—Equal contribution

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Notes

  1. 1.

    This sample set is also used within §3.2, as indicated in Fig. 2.

  2. 2.

    Note that we also experimented with both rounding up and rounding to the nearest integer value for Eq. (1), but found that rounding down gave the best performance.

  3. 3.

    The domain names retrieved are equal to highest-level categories as defined in the arXiv category taxonomy: https://arxiv.org/category_taxonomy.

  4. 4.

    Increasing or decreasing K (which directly influences the number of sentences in the summaries produced by DodoRank) invariably led to a worse average performance, as measured by the metrics described in this Section.

  5. 5.

    All ROUGE calculations are performed using the rouge-score Python package.

  6. 6.

    Judges are native English speakers holding a bachelor’s degree in scientific disciplines.

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Acknowledgements

This work was supported by the Centre for Doctoral Training in Speech and Language Technologies (SLT) and their Applications funded by UK Research and Innovation [grant number EP/S023062/1].

Author information

Authors and Affiliations

  1. University of Sheffield, Sheffield, UK

    Tomas Goldsack, Chenghua Lin & Carolina Scarton

  2. Beihang University, Beijing, China

    Zhihao Zhang

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  1. Tomas Goldsack
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  2. Zhihao Zhang
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  3. Chenghua Lin
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  4. Carolina Scarton
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Corresponding author

Correspondence to Chenghua Lin .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Goldsack, T., Zhang, Z., Lin, C., Scarton, C. (2023). Domain-Driven and Discourse-Guided Scientific Summarisation. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_23

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