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MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles

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From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries (ICADL 2022)

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Abstract

Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations. Scholarly information extraction has various applications in exploration, archival, and curation services for digital libraries and knowledge management systems. We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles. Our approach condenses the article’s full-text to property-value pairs as a segmented text snippet called structured summary. We also present a sizable scholarly dataset combining structured summaries retrieved from a scholarly knowledge graph and corresponding publicly available scientific articles, which we openly publish as a resource for the research community. Our results show that structured summarization is a suitable approach for targeted information extraction that complements other commonly used methods such as question answering and named entity recognition.

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Notes

  1. 1.

    Code & data (with stats): https://github.com/YaserJaradeh/MORTY.

  2. 2.

    https://www.nlm.nih.gov/databases/download/pubmed_medline.html.

  3. 3.

    Data snapshot was taken on 02.02.2022.

  4. 4.

    Properties that are used solely for information organization and have no semantic value.

  5. 5.

    https://paperswithcode.com/sota/text-summarization-on-pubmed-1.

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Author information

Authors and Affiliations

  1. L3S Research Center, Leibniz University, Hannover, Germany

    Mohamad Yaser Jaradeh

  2. Leibniz Information Centre for Science and Technology, Hanover, Germany

    Markus Stocker & Sören Auer

Authors
  1. Mohamad Yaser Jaradeh
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  2. Markus Stocker
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  3. Sören Auer
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Correspondence to Mohamad Yaser Jaradeh .

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Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Yuen-Hsien Tseng

  2. Doshisha University, Kyoto, Japan

    Marie Katsurai

  3. VNU University of Engineering and Technology, Hanoi, Vietnam

    Hoa N. Nguyen

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Jaradeh, M.Y., Stocker, M., Auer, S. (2022). MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles. In: Tseng, YH., Katsurai, M., Nguyen, H.N. (eds) From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries. ICADL 2022. Lecture Notes in Computer Science, vol 13636. Springer, Cham. https://doi.org/10.1007/978-3-031-21756-2_23

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  • DOI: https://doi.org/10.1007/978-3-031-21756-2_23

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