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A joint framework for identifying the type and arguments of scientific contribution

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Abstract

Scientific contribution is typically embodiment of the value of a scientific publication, which reflects the inspiration, promotion, and improvement of the publication on existing theories or guiding practices. To analyze scientific contribution efficiently, in this paper, we introduce the task of automatically identifying the contribution type and their corresponding arguments. For this novel task, we first construct a new dataset SciContri by manually annotating the contribution type and argument information of 783 scientific articles. And we propose a joint framework named SciContriExt for the scientific contribution extraction task, i.e., classifying the contribution type and extracting corresponding fine-grained arguments. Our proposed framework adopts a deep learning classification model and a extraction model which extracts the contribution arguments from both token and span level. We jointly train the classification and extraction models by performing a weighted summation of the loss functions of the two models. Experiments show that our proposed model outperforms the state-of-the-art approaches on both contribution type classification and argument extraction tasks. The SciContri dataset will be released for future research.

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  1. https://arxiv.org.

  2. https://grobid.readthedocs.io/en/latest/.

  3. https://brat.nlplab.org/.

  4. https://github.com/hanxiao/bert-as-service.

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Funding

This work was supported by National Natural Science Foundation of China (No. 61976221) and Open Project Foundation of Key Laboratory of Intelligent Information Processing of Shanxi Province (under Grant CICIP2020002).

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Author notes

  1. Wenhan Chao and Mengyuan Chen have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, People’s Republic of China

    Wenhan Chao & Mengyuan Chen

  2. PLA Academy of Military Sciences, Beijing, 100142, People’s Republic of China

    Xian Zhou & Zhunchen Luo

Authors
  1. Wenhan Chao
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  2. Mengyuan Chen
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  3. Xian Zhou
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  4. Zhunchen Luo
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Correspondence to Xian Zhou or Zhunchen Luo.

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Chao, W., Chen, M., Zhou, X. et al. A joint framework for identifying the type and arguments of scientific contribution. Scientometrics 128, 3347–3376 (2023). https://doi.org/10.1007/s11192-023-04694-6

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