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A context-aware citation recommendation model with BERT and graph convolutional networks

Author

Listed:
  • Chanwoo Jeong

    (Gachon University)

  • Sion Jang

    (Gachon University)

  • Eunjeong Park

    (Papago, NAVER)

  • Sungchul Choi

    (Gachon University)

Abstract

With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, the context-aware citation recommendation has been researched for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets, and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning-based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder. For this, we use graph convolutional networks layer, and bidirectional encoder representations from transformers, a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, this dataset is the first well-organized dataset for a context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision and recall@k.

Suggested Citation

  • Chanwoo Jeong & Sion Jang & Eunjeong Park & Sungchul Choi, 2020. "A context-aware citation recommendation model with BERT and graph convolutional networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1907-1922, September.
  • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03561-y
    DOI: 10.1007/s11192-020-03561-y
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    References listed on IDEAS

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    1. Meen Chul Kim & Chaomei Chen, 2015. "A scientometric review of emerging trends and new developments in recommendation systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 239-263, July.
    2. Moed, Henk F., 2010. "Measuring contextual citation impact of scientific journals," Journal of Informetrics, Elsevier, vol. 4(3), pages 265-277.
    3. Bai, Xiaomei & Zhang, Fuli & Lee, Ivan, 2019. "Predicting the citations of scholarly paper," Journal of Informetrics, Elsevier, vol. 13(1), pages 407-418.
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    Citations

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    Cited by:

    1. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
    2. Khalid Haruna & Maizatul Akmar Ismail & Atika Qazi & Habeebah Adamu Kakudi & Mohammed Hassan & Sanah Abdullahi Muaz & Haruna Chiroma, 2020. "Research paper recommender system based on public contextual metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 101-114, October.
    3. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Kaiwen Shi & Kan Liu & Xinyan He, 2024. "Heterogeneous hypergraph learning for literature retrieval based on citation intents," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4167-4188, July.
    5. Chanathip Pornprasit & Xin Liu & Pattararat Kiattipadungkul & Natthawut Kertkeidkachorn & Kyoung-Sook Kim & Thanapon Noraset & Saeed-Ul Hassan & Suppawong Tuarob, 2022. "Enhancing citation recommendation using citation network embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 233-264, January.
    6. Xiaojuan Zhang & Shuqi Song & Yuping Xiong, 2024. "Personalized global citation recommendation with diversification awareness," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3625-3657, July.
    7. Hei-Chia Wang & Jen-Wei Cheng & Che-Tsung Yang, 2022. "SentCite: a sentence-level citation recommender based on the salient similarity among multiple segments," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2521-2546, May.
    8. Jialiang Lin & Yao Yu & Jiaxin Song & Xiaodong Shi, 2022. "Detecting and analyzing missing citations to published scientific entities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2395-2412, May.
    9. Moreno La Quatra & Luca Cagliero & Elena Baralis, 2021. "Leveraging full-text article exploration for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8275-8293, October.
    10. Antonina Dattolo & Marco Corbatto, 2022. "Assisting researchers in bibliographic tasks: A new usable, real‐time tool for analyzing bibliographies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 757-776, June.
    11. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2023. "Context-aware citation recommendation of scientific papers: comparative study, gaps and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4243-4268, August.
    12. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    13. Diego Kozlowski & Jennifer Dusdal & Jun Pang & Andreas Zilian, 2021. "Semantic and relational spaces in science of science: deep learning models for article vectorisation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5881-5910, July.
    14. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
    15. Yonghe Lu & Meilu Yuan & Jiaxin Liu & Minghong Chen, 2023. "Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1367-1393, February.

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