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Cited text span identification for scientific summarisation using pre-trained encoders

Author

Listed:
  • Chrysoula Zerva

    (University of Manchester)

  • Minh-Quoc Nghiem

    (University of Manchester)

  • Nhung T. H. Nguyen

    (University of Manchester)

  • Sophia Ananiadou

    (University of Manchester
    Alan Turing Institute)

Abstract

We present our approach for the identification of cited text spans in scientific literature, using pre-trained encoders (BERT) in combination with different neural networks. We further experiment to assess the impact of using these cited text spans as input in BERT-based extractive summarisation methods. Inspired and motivated by the CL-SciSumm shared tasks, we explore different methods to adapt pre-trained models which are tuned for generic domain to scientific literature. For the identification of cited text spans, we assess the impact of different configurations in terms of learning from augmented data and using different features and network architectures (BERT, XLNET, CNN, and BiMPM) for training. We show that identifying and fine-tuning the language models on unlabelled or augmented domain specific data can improve the performance of cited text span identification models. For the scientific summarisation we implement an extractive summarisation model adapted from BERT. With respect to the input sentences taken from the cited paper, we explore two different scenarios: (1) consider all the sentences (full-text) of the referenced article as input and (2) consider only the text spans that have been identified to be cited by other publications. We observe that in certain experiments, by using only the cited text-spans we can achieve better performance, while minimising the input size needed.

Suggested Citation

  • Chrysoula Zerva & Minh-Quoc Nghiem & Nhung T. H. Nguyen & Sophia Ananiadou, 2020. "Cited text span identification for scientific summarisation using pre-trained encoders," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3109-3137, December.
  • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03455-z
    DOI: 10.1007/s11192-020-03455-z
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    References listed on IDEAS

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    1. B Ian Hutchins & Xin Yuan & James M Anderson & George M Santangelo, 2016. "Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level," PLOS Biology, Public Library of Science, vol. 14(9), pages 1-25, September.
    2. Unknown, 2016. "Proceedings Of Abstracts," 152nd Seminar, August 30 - September 1, 2016, Novi Sad, Serbia 244068, European Association of Agricultural Economists.
    3. Saeed-Ul Hassan & Mubashir Imran & Sehrish Iqbal & Naif Radi Aljohani & Raheel Nawaz, 2018. "Deep context of citations using machine-learning models in scholarly full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1645-1662, December.
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    Cited by:

    1. 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.

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