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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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References

  1. Ahn D (2006) The stages of event extraction. In: The workshop on annotating & reasoning about time & events, pp 1–8

  2. Blanco R, Lioma C (2012) Graph-based term weighting for information retrieval. In Retr 15(1):54–92

    Article  Google Scholar 

  3. Boguraev B, Ando R K (2005) Timeml-compliant text analysis for temporal reasoning. In: International joint conference on artificial intelligence, pp 997–1003

  4. Che W, Li Z, Liu T (2012) Ltp: A chinese language technology platform. J Chin Inf Process 2(6):13–16

    Article  Google Scholar 

  5. Hahn U, Mani I (2000) The challenges of automatic summarization. Computer 33(11):29–36

    Article  Google Scholar 

  6. Hai L C, Ng H T (2002) A maximum entropy approach to information extraction from semi-structured and free text. In: 18th national conference on Artificial intelligence, pp 786–791

  7. Hogenboom F, Frasincar F, Kaymak U, Jong F D, Caron E (2016) A survey of event extraction methods from text for decision support systems. Decis Support Syst 85(C):12–22

    Article  Google Scholar 

  8. Kim J T, Dan I M (1993) Palka:a system for lexical knowledge acquisition. In: DBLP, pp 124–131

  9. Kim J T, Moldovan D I (1995) Acquisition of linguistic patterns for knowledge-based information extraction. IEEE Trans Knowl Data Eng 7(5):713–724

    Article  Google Scholar 

  10. Kuksin S B, Shirikyan A (2010) Event extraction for systems biology by text mining the iterature. Trends Biotechnol 28(7):381

    Article  Google Scholar 

  11. Lee C S, Chen Y J, Jian Z W (2003) Ontology-based fuzzy event extraction agent for chinese e-news summarization. Expert Syst Appl 25(3):431–447

    Article  Google Scholar 

  12. Liang H, Chen Q X, Ping-Bo W U (2006) Information extraction system based on event frame. J Chin Inf Process 20(2):40–46

    Google Scholar 

  13. Lin C Y (2004) Looking for a few good metrics : Rouge and its evaluation. In: Proc. of the Ntcir workshops, Tokyo, Japan

  14. Mihalcea R, Tarau P (2004) Textrank: Bringing order into texts. Unt Scholarly Works, pp 404–411

  15. Minsky M (1974) A framework for representing knowledge, Massachusetts Institute of Technology

  16. Piskorski J, Tanev H, Wennerberg P O (2007) Extracting violent events from on-line news for ontology population. In: International conference on business information systems, pp 287–300

  17. Pustejovsky J, Castano J M, Ingria R, Sauri R, Gaizauskas R J, Setzer A, Katz G, Radev D R (2003) Timeml: Robust specification of event and temporal expressions in text. Direct Quest Answer 3:28–34

    Google Scholar 

  18. Sarawagi S (2008) Information extraction. Springer, Berlin

    MATH  Google Scholar 

  19. Teufel S, Moens M (2002) Articles summarizing scientific articles: Experiments with relevance and rhetorical status. Comput Linguist 28(4). http://www.aclweb.org/anthology/J02-4002

  20. Xu F, Uszkoreit H, Li H (2006) Automatic event and relation detection with seeds of varying complexity. Proceedings of Aaai workshop event extraction & synthesis

  21. Yankova M, Boytcheva S (2003) Focusing on scenario recognition in information extraction. In: 10th conference on European chapter of the association for computational linguistics, pp 41–48

  22. Zhang J, Li K, Yao C (2018) Event-based summarization for scientific literature in chinese. Proc Comput Sci 129:88–92. https://doi.org/10.1016/j.procs.2018.03.052. https://www.sciencedirect.com/science/article/pii/S1877050918302758, 2017 International Conference on Identification, Information and Knowledge in the Internet of Things

    Article  Google Scholar 

  23. Zhang J, Sun Y, Gao Y, Fang X (2019) Event-centric machine strategic reading approach for scientific literature. In: Rongfang Bie Yunchuan Sun JY (ed) 2018 international conference on identification, information and knowledge in the internet of things, Procedia computer science, vol 147, pp 44–48. Elsevier. https://doi.org/10.1016/j.procs.2019.01.182

  24. Zhang J, Sun Y, Jara AJ (2015) Towards semantically linked multilingual corpus. Int J Inf Manag 35(3):387–395. https://doi.org/10.1016/j.ijinfomgt.2015.01.004. http://www.sciencedirect.com/science/article/pii/S0268401215000055

    Article  Google Scholar 

  25. Zhang J, Sun Y, Yao C (2017) Semantically linking events for massive scientific literature research. The Electronic Library 35(4):724–744. https://doi.org/10.1108/EL-09-2016-0198

    Article  Google Scholar 

  26. Zhang J, Yao C, Sun Y, Fang Z (2016) Building text-based temporally linked event network for scientific big data analytics. Pers Ubiquit Comput 20 (5):743–755. https://doi.org/10.1007/s00779-016-0940-x

    Article  Google Scholar 

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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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Cite this article

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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