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Enhancing keyphrase extraction from academic articles with their reference information

Author

Listed:
  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

  • Lei Zhao

    (Nanjing University of Science and Technology)

  • Mengyuan Zhao

    (Nanjing University of Science and Technology)

  • Yingyi Zhang

    (Nanjing University of Science and Technology)

Abstract

With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Naïve Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the research on automatic keyphrase extraction.

Suggested Citation

  • Chengzhi Zhang & Lei Zhao & Mengyuan Zhao & Yingyi Zhang, 2022. "Enhancing keyphrase extraction from academic articles with their reference information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 703-731, February.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:2:d:10.1007_s11192-021-04230-4
    DOI: 10.1007/s11192-021-04230-4
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    References listed on IDEAS

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    1. Shimelis G. Assefa & Abebe Rorissa, 2013. "A bibliometric mapping of the structure of STEM education using co‐word analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(12), pages 2513-2536, December.
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    4. Liu Yang & Keping Li & Hangfei Huang, 2018. "A new network model for extracting text keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 339-361, July.
    5. Yingyi Zhang & Chengzhi Zhang, 2021. "Enhancing keyphrase extraction from microblogs using human reading time," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 611-626, May.
    6. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

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    2. Jinqing Yang & Zhifeng Liu & Xiufeng Cheng & Guanghui Ye, 2024. "Understanding the keyword adoption behavior patterns of researchers from a functional structure perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3359-3384, June.
    3. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).

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