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Intent-Controllable Citation Text Generation

Author

Listed:
  • Shing-Yun Jung

    (Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

  • Ting-Han Lin

    (Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

  • Chia-Hung Liao

    (Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

  • Shyan-Ming Yuan

    (Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

  • Chuen-Tsai Sun

    (Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

Abstract

We study the problem of controllable citation text generation by introducing a new concept to generate citation texts. Citation text generation, as an assistive writing approach, has drawn a number of researchers’ attention. However, current research related to citation text generation rarely addresses how to generate the citation texts that satisfy the specified citation intents by the paper’s authors, especially at the beginning of paper writing. We propose a controllable citation text generation model that extends a pre-trained sequence to sequence models, namely, BART and T5, by using the citation intent as the control code to generate the citation text, meeting the paper authors’ citation intent. Experimental results demonstrate that our model can generate citation texts semantically similar to the reference citation texts and satisfy the given citation intent. Additionally, the results from human evaluation also indicate that incorporating the citation intent may enable the models to generate relevant citation texts almost as scientific paper authors do, even when only a little information from the citing paper is available.

Suggested Citation

  • Shing-Yun Jung & Ting-Han Lin & Chia-Hung Liao & Shyan-Ming Yuan & Chuen-Tsai Sun, 2022. "Intent-Controllable Citation Text Generation," Mathematics, MDPI, vol. 10(10), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1763-:d:820768
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    References listed on IDEAS

    as
    1. Iñaki Ucar & Felipe López-Fernandino & Pablo Rodriguez-Ulibarri & Laura Sesma-Sanchez & Veronica Urrea-Micó & Joaquín Sevilla, 2014. "Growth in the number of references in engineering journal papers during the 1972–2013 period," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1855-1864, March.
    2. Boyack, Kevin W. & van Eck, Nees Jan & Colavizza, Giovanni & Waltman, Ludo, 2018. "Characterizing in-text citations in scientific articles: A large-scale analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 59-73.
    3. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    4. Jeppe Nicolaisen & Tove Faber Frandsen, 2021. "Number of references: a large-scale study of interval ratios," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 259-285, January.
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