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Cited text spans identification with an improved balanced ensemble model

Author

Listed:
  • Pancheng Wang

    (National University of Defense Technology)

  • Shasha Li

    (National University of Defense Technology)

  • Haifang Zhou

    (National University of Defense Technology)

  • Jintao Tang

    (National University of Defense Technology)

  • Ting Wang

    (National University of Defense Technology)

Abstract

Scientific summarization aims to provide condensed summary of important contributions of scientific papers. This problem has been extensively explored and recent interest has been aroused to taking advantage of the cited text spans to generate summaries. Cited text spans are the texts in the cited paper that most accurately reflect the citation. They can be viewed as important aspects of the cited paper which are annotated by academic community. Hence, identifying cited text spans is of vital importance for providing a different scientific summarization. In this paper, we explore three potential improvements towards our previous work which is a two-layer ensemble model to tackle the cited text spans identification problem. We first view cited text spans identification as an imbalanced classification problem and carry out comparison on preprocessing methods to handle the imbalanced dataset. Then we propose RANdom Sampling Aggregating (RANSA) algorithm to train classifiers in the first ensemble layer model. Finally, an improved stacking framework Hybrid-Stacking is applied to combine the models of the first layer. Our new ensemble model overcomes flaws of the previous work, and shows improved performance on cited text spans identification.

Suggested Citation

  • Pancheng Wang & Shasha Li & Haifang Zhou & Jintao Tang & Ting Wang, 2019. "Cited text spans identification with an improved balanced ensemble model," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1111-1145, September.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:3:d:10.1007_s11192-019-03167-z
    DOI: 10.1007/s11192-019-03167-z
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    References listed on IDEAS

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    1. Qing Cheng & Xin Lu & Zhong Liu & Jincai Huang, 2015. "Mining research trends with anomaly detection models: the case of social computing research," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 453-469, May.
    2. Dragomir R. Radev & Mark Thomas Joseph & Bryan Gibson & Pradeep Muthukrishnan, 2016. "A bibliometric and network analysis of the field of computational linguistics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(3), pages 683-706, March.
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    Cited by:

    1. Li Zhang & Ming Liu & Bo Wang & Bo Lang & Peng Yang, 2021. "Discovering communities based on mention distance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1945-1967, March.

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