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Event-based summarization method for scientific literature

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Abstract

Massive scientific and technical literature has recorded the developments of science and technology and contains plentiful knowledge. Researchers have to read scientific literature such as papers, patents, and reports to know the latest developments in time. However, it is difficult for researchers to read all the newly published and relevant literature. So there is an urgent need for scientific literature summarization systems to provide brief and important dynamic information that researchers are interested in. This paper proposes an approach to generate automatic summarization based on 5W1H event structure. Sentences in the literature are classified and selected for different elements of events by relevance, and then the importance of each candidate sentence is calculated. Top-k relevant and important sentences are selected to formulate event-based summarization. Comparing with existing summarization results or abstracts given by authors, experiment results of our approach contain more detailed information with the the 5W1H event structure, which is more convenient for researchers to search and browse the brief description of scientific and technical information distributed in massive scientific literature.

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Funding

This research work was partially supported by National Natural Science of China (Grant No. 71503240, 61371185 and 61202436).

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Authors and Affiliations

  1. Institute of Scientific and Technical Information of China, Beijing, 100038, China

    Junsheng Zhang, Kun Li & Changqing Yao

  2. Business School, Beijing Normal University, Beijing, 100875, China

    Yunchuan Sun

Authors
  1. Junsheng Zhang
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  2. Kun Li
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  3. Changqing Yao
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  4. Yunchuan Sun
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Correspondence to Yunchuan Sun.

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Bibliographic note

Portions of this work were previously presented in our previous work [22]. The current article extends this work in several ways, most notably: (1) We add the detailed method for event-based summarization for scientific literature; (2) We add evaluation experiments and compare with existing summarization systems.

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Zhang, J., Li, K., Yao, C. et al. Event-based summarization method for scientific literature. Pers Ubiquit Comput 25, 959–968 (2021). https://doi.org/10.1007/s00779-019-01301-5

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  • DOI: https://doi.org/10.1007/s00779-019-01301-5

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