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Leveraging MRC Framework for Research Contribution Patterns Identification in Citation Sentences

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Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration (ICADL 2023)

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Abstract

Research contributions convey the essence of academic papers, highlighting their novel knowledge and understanding compared to prior research. In this study, we address the challenge of identifying research contribution patterns from citation sentences by leveraging a Machine Reading Comprehension (MRC) framework. The MRC approach formulates the extraction of contribution patterns as a question-answering task, utilizing natural language queries to extract contribution patterns (CONTRIBUTION, INFLUENCE, and FIELD) from the context.

Our method outperforms the SOTA NER approach in 2022: W2NER, achieving significant performance improvements of +23.76% and +31.92% in F1 scores for label and entity recognition, respectively. In addition, through manual validation and comparison with ChatGPT annotation results, we demonstrate that the accuracy of our approach is 21.65% higher in identifying research contribution patterns. Moreover, the MRC framework handles nested entities and resolves reference disambiguation more accurately, providing a robust solution for complex citation sentences.

Overall, our work presents an advanced approach for identifying research contribution patterns from citation sentences, showcasing its potential to enhance information retrieval and understanding within the scientific community.

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Notes

  1. 1.

    https://www.nobelprize.org/prizes/medicine/2021/press-release/.

  2. 2.

    https://openai.com/blog/chatgpt/.

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Acknowledgments

This work was supported by the major project of the National Social Science Foundation of China “Big Data-driven Semantic Evaluation System of Science and Technology Literature” (Project No. 21&ZD329).

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Authors and Affiliations

  1. National Science Library, Chinese Academy of Science, No. 33, Beisihuan Xilu, Beijing, China

    Yang Zhao & Zhixiong Zhang

  2. Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, No. 33 Beisihuan Xilu, Beijing, China

    Yang Zhao & Zhixiong Zhang

  3. School of Information Resource Management, Renmin University of China, No. 59, Zhongguancun Street, Haidian District, Beijing, China

    Yue Xiao

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  1. Yang Zhao
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  2. Zhixiong Zhang
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  3. Yue Xiao
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Correspondence to Zhixiong Zhang .

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Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Dion H. Goh

  2. Academia Sinica, Taipei, Taiwan

    Shu-Jiun Chen

  3. Mahidol University, Tambon Salaya, Amphoe Phutthamonthon, Thailand

    Suppawong Tuarob

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Zhao, Y., Zhang, Z., Xiao, Y. (2023). Leveraging MRC Framework for Research Contribution Patterns Identification in Citation Sentences. In: Goh, D.H., Chen, SJ., Tuarob, S. (eds) Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. ICADL 2023. Lecture Notes in Computer Science, vol 14458. Springer, Singapore. https://doi.org/10.1007/978-981-99-8088-8_16

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  • DOI: https://doi.org/10.1007/978-981-99-8088-8_16

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  • Print ISBN: 978-981-99-8087-1

  • Online ISBN: 978-981-99-8088-8

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