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Automatic Simplification of Scientific Texts: SimpleText Lab at CLEF-2022

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Advances in Information Retrieval (ECIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13186))

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Abstract

The Web and social media have become the main source of information for citizens, with the risk that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. Non-experts tend to avoid scientific literature due to its complex language or their lack of prior background knowledge. Text simplification promises to remove some of these barriers. The CLEF 2022 SimpleText track addresses the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of NLP and IR researchers working together to resolve one of the greatest challenges of today. The track will use a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.

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Notes

  1. 1.

    https://easycovid19.org/.

  2. 2.

    https://papiermachesciences.org/.

  3. 3.

    https://www.reddit.com/r/explainlikeimfive.

  4. 4.

    https://sciencebites.org/.

  5. 5.

    https://stellargraph.readthedocs.io/.

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Acknowledgments

We thank Alain Kerhervé, University Translation Office, master students in Translation from the Université de Bretagne Occidentale, and the MaDICS research group.

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Authors and Affiliations

  1. Université de Bretagne Occidentale, HCTI, EA 4249, Bretagne, France

    Liana Ermakova, Elise Mathurin & Nicolas Poinsu

  2. Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France

    Patrice Bellot

  3. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  4. Institut National des Sciences Appliquées de Lyon, LIRIS UMR 5205 CNRS, Lyon, France

    Diana Nurbakova

  5. Sechenov University, Moscow, Russia

    Irina Ovchinnikova

  6. Avignon Université, LIA, Avignon, France

    Eric SanJuan & Stéphane Huet

  7. University of Minho, Braga, Portugal

    Sílvia Araújo

  8. Université de Bretagne Sud, HCTI, EA 4249, Bretagne, France

    Radia Hannachi

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  1. Liana Ermakova
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Correspondence to Liana Ermakova .

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  1. Martin Luther University Halle-Wittenberg, Halle, Germany

    Matthias Hagen

  2. Leiden University, Leiden, The Netherlands

    Suzan Verberne

  3. University of Glasgow, Glasgow, UK

    Craig Macdonald

  4. University of Duisburg-Essen, Essen, Germany

    Christin Seifert

  5. University of Stavanger, Stavanger, Norway

    Krisztian Balog

  6. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  7. University of Stavanger, Stavanger, Norway

    Vinay Setty

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Ermakova, L. et al. (2022). Automatic Simplification of Scientific Texts: SimpleText Lab at CLEF-2022. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_46

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