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| Contribution Attribution Based on Semantic Similarity: Revealing Implicitlyd Sources in Directs |
| Yang Siluo1,2, Wu Lijuan1,2, Wu Biyao1,2 |
1.School of Information Management, Wuhan University, Wuhan 430072
2.Research Center for Chinese Science Evaluation, Wuhan University, Wuhan 430072 |
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Abstract To accurately identify the hidden contributions and unlabeled implicitly cited sources in direct citations, in this paper, citation contribution attribution (CCA) methods are constructed based on citation markers and semantic similarity, and the necessity of integrating hidden contributions into academic evaluation is discussed to provide a new perspective for research contribution attribution. Using the CL-SciSumm 2017 dataset as an empirical object, two types of CCA methods are used to calculate the explicit contributions of focal papers. The remaining contributions are defined as hidden contributions of their references and allocated according to the equal allocation principle and semantic similarity. Two traditional methods, direct citation evaluation and citation cascade evaluation, are employed as benchmarks to compare the differences in contribution scores of implicitly cited papers. The references of focal papers provide hidden contributions to their citing papers. Semantic similarity-based CCA methods identify implicitly cited sources more comprehensively but need further improvement in identification precision. The contribution scores obtained by the proposed and traditional methods differ significantly, suggesting that the hidden contributions should be incorporated into the academic contribution evaluation system of scientific literature.
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Received: 08 March 2025
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