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Enhancing keyphrase extraction from academic articles using section structure information

Author

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  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

  • Xinyi Yan

    (Nanjing University of Science and Technology)

  • Lei Zhao

    (Nanjing University of Science and Technology)

  • Yingyi Zhang

    (Soochow University)

Abstract

The exponential increase in academic papers has significantly increased the time required for researchers to access relevant literature. Keyphrase extraction (KPE) offers a solution to this situation by enabling researchers to efficiently retrieve relevant literature. The current study on KPE from academic articles aims to improve the performance of extraction models through innovative approaches using Title and Abstract as input corpora. However, the semantic richness of keywords is significantly constrained by the length of the abstract. While full-text-based KPE can address this issue, it simultaneously introduces noise, which significantly diminishes KPE performance. To address this issue, this paper utilized the structural features and section texts obtained from the section structure information of academic articles to extract keyphrase from academic papers. The approach consists of two main parts: (1) exploring the effect of seven structural features on KPE models, and (2) integrating the extraction results from all section texts used as input corpora for KPE models via a keyphrase integration algorithm to obtain the keyphrase integration result. Furthermore, this paper also examined the effect of the classification quality of section structure on the KPE performance. The results show that incorporating structural features improves KPE performance, though different features have varying effects on model efficacy. The keyphrase integration approach yields the best performance, and the classification quality of section structure can affect KPE performance. These findings indicate that using the section structure information of academic articles contributes to effective KPE from academic articles. The code and dataset supporting this study are available at https://github.com/yan-xinyi/SSB_KPE .

Suggested Citation

  • Chengzhi Zhang & Xinyi Yan & Lei Zhao & Yingyi Zhang, 2025. "Enhancing keyphrase extraction from academic articles using section structure information," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(4), pages 2311-2343, April.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:4:d:10.1007_s11192-025-05286-2
    DOI: 10.1007/s11192-025-05286-2
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    References listed on IDEAS

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    1. Ding, Ying & Liu, Xiaozhong & Guo, Chun & Cronin, Blaise, 2013. "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, Elsevier, vol. 7(3), pages 583-592.
    2. Jin Yao Chin & Sourav S. Bhowmick & Adam Jatowt, 2019. "On‐demand recent personal tweets summarization on mobile devices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(6), pages 547-562, June.
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    4. 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.
    5. Bowen Ma & Chengzhi Zhang & Yuzhuo Wang & Sanhong Deng, 2022. "Enhancing identification of structure function of academic articles using contextual information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 885-925, February.
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