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Identifying Influential References in Scholarly Papers Using Citation Contexts

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

Abstract

Citation count is commonly used as a straightforward metric for measuring the impact of a paper. However, since all citations are treated equally, citation count does not accurately capture the true influence of a particular cited paper on the citing paper. To accurately measure the individual impact of cited papers, it is required to identify those that have a high influence on a citing paper. This paper proposes a method to identify the influential citations using the text of citation contexts, specifically the citing sentences. Citing sentences contain the descriptions of the cited papers and the relationship between the citing paper and each cited paper. The proposed method extracts the descriptions of cited papers from the citing sentences and utilizes them to identify influential references. Experimental results have shown the benefits of using the extracted description of each cited paper.

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Acknowledgements

This work was partially supported by the Grant-in-Aid for Challenging Research (Exploratory) (No. 23K18506) of JSPS and by JST SPRING, Grant Number JPMJSP2125. The computation was carried out on supercomputer “Flow” at Information Technology Center, Nagoya University.

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Correspondence to Tomoki Ikoma or Shigeki Matsubara .

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Ikoma, T., Matsubara, S. (2023). Identifying Influential References in Scholarly Papers Using Citation Contexts. 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_13

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  • DOI: https://doi.org/10.1007/978-981-99-8088-8_13

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