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Characterizing human summarization strategies for text reuse and transformation in literature review writing

Author

Listed:
  • Kokil Jaidka

    (Nanyang Technological University)

  • Christopher S. G. Khoo

    (Nanyang Technological University)

  • Jin-Cheon Na

    (Nanyang Technological University)

Abstract

Citations are useful signals of information salience, but little research has identified the patterns of information selection, transformation, and organization that they espouse. This paper investigated the summarization strategies followed in the writing of literature review sections of information science research papers. We found that the summarization strategies followed are different for the two major styles of literature review writing, descriptive versus integrative literature reviews. Descriptive literature reviews, which focus on individual descriptions of research papers, are more likely to reference the Method and the Result sections of the cited paper and copy-paste text the referenced text. In contrast, integrative literature reviews, which synthesize the main ideas for many papers together, have more critiques and focus mainly on the Conclusion sections. These findings, based on a hand-annotated dataset, have the potential to scale up into a transformation-invariant neural architecture for scientific summarization that can generate different summaries of the input text with integrative or descriptive characteristics.

Suggested Citation

  • Kokil Jaidka & Christopher S. G. Khoo & Jin-Cheon Na, 2019. "Characterizing human summarization strategies for text reuse and transformation in literature review writing," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1563-1582, December.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:3:d:10.1007_s11192-019-03250-5
    DOI: 10.1007/s11192-019-03250-5
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    2. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    3. Aaron Elkiss & Siwei Shen & Anthony Fader & Güneş Erkan & David States & Dragomir Radev, 2008. "Blind men and elephants: What do citation summaries tell us about a research article?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 51-62, January.
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