Abstract Citation analysis, an essential method in bibliometrics and scientometrics, has been widely applied to assess scholarly publications. Traditionally, studies attribute citation contributions directly to cited papers, overlooking indirect citations. Citation cascade research improves on this by assuming papers inherit contributions from their citation generations but does not consider the source of the citation content (CC). We argue that citation contributions should be attributed to the source of the CC, which includes not only the cited paper but also its references. This study introduces a semantic similarity‐driven approach (CCASS RS) to allocate citation contributions. CCASS RS evaluates semantic similarity between CC in the citing paper (CC FP) and the reference span in the cited paper (RS FP), as well as between CC FP and CCs associated with the cited paper's references (CC FPR). If similarity between CC FP and CC FPRi exceeds or equals that between CC FP and RS FP, the i‐ th reference is credited; otherwise, the cited paper receives full credit. Tested on the CL‐SciSumm 2017 dataset, CCASS RS outperformed three established methods in identifying implicit cited sources, enabling references to receive varying contributions based on semantic similarity. This study highlights the significant impact of citation contribution attribution on paper evaluation and ranking.
Yang et al. (Fri,) studied this question.