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Citance-based retrieval and summarization using IR and machine learning

Author

Listed:
  • Samaneh Karimi

    (University of Tehran
    University of Houston)

  • Luis Moraes

    (University of Houston)

  • Avisha Das

    (University of Houston)

  • Azadeh Shakery

    (University of Tehran
    University of Tehran)

  • Rakesh Verma

    (University of Houston)

Abstract

We consider the three interesting problems posed by the CL-SciSumm series of shared tasks. Given a reference document D and a set $$C_D$$ C D of citances for D: (1) find the span of reference text that corresponds to each citance $$c \in C_D$$ c ∈ C D , (2) identify the facet corresponding to each span of reference text from a predefined list of five facets, and (3) construct a summary of at most 250 words for D based on the reference spans. The shared task provided annotated training and test sets for these problems. This paper describes our efforts and the results achieved for each problem, and also a discussion of some interesting parameters of the datasets, which may spur further improvements and innovations.

Suggested Citation

  • Samaneh Karimi & Luis Moraes & Avisha Das & Azadeh Shakery & Rakesh Verma, 2018. "Citance-based retrieval and summarization using IR and machine learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1331-1366, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2785-8
    DOI: 10.1007/s11192-018-2785-8
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    References listed on IDEAS

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    1. Unknown, 2016. "Proceedings Of Abstracts," 152nd Seminar, August 30 - September 1, 2016, Novi Sad, Serbia 244068, European Association of Agricultural Economists.
    2. 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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    Cited by:

    1. Guillaume Cabanac & Ingo Frommholz & Philipp Mayr, 2018. "Bibliometric-enhanced information retrieval: preface," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1225-1227, August.
    2. Saeed-Ul Hassan & Naif R. Aljohani & Mudassir Shabbir & Umair Ali & Sehrish Iqbal & Raheem Sarwar & Eugenio Martínez-Cámara & Sebastián Ventura & Francisco Herrera, 2020. "Tweet Coupling: a social media methodology for clustering scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 973-991, August.
    3. Iqra Safder & Saeed-Ul Hassan, 2019. "Bibliometric-enhanced information retrieval: a novel deep feature engineering approach for algorithm searching from full-text publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 257-277, April.
    4. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
    5. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.

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