@inproceedings{jantsch-etal-2025-finecite,
    title = "{F}ine{C}ite: A Novel Approach For Fine-Grained Citation Context Analysis",
    author = "Jantsch, Lasse M.  and
      Koh, Dong-Jae  and
      Yoon, Seonghwan  and
      Lee, Jisu  and
      Lauscher, Anne  and
      Suh, Young-Kyoon",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1259/",
    pages = "24525--24542",
    ISBN = "979-8-89176-256-5",
    abstract = "Citation context analysis (CCA) is a field of research studying the role and purpose of citation in scientific discourse. While most of the efforts in CCA have been focused on elaborate characterization schemata to assign function or intent labels to individual citations, the citation context as the basis for such a classification has received rather limited attention. This relative neglect, however, has led to the prevalence of vague definitions and restrictive assumptions, limiting the citation context in its expressiveness. It is a common practice, for example, to restrict the context to the citing sentence. While this simple context conceptualization might be sufficient to assign intent or function classes, it fails to cover the rich information of scientific discourse. To address this concern, we analyze the context conceptualizations of previous works and, to our knowledge, construct the first comprehensive context definition based on the semantic properties of the citing text. To evaluate this definition, we construct and publish the FineCite corpus containing 1,056 manually annotated citation contexts. Our experiments on established CCA benchmarks demonstrate the effectiveness of our fine-grained context definition, showing improvements of up to 25{\%} compared to state-of-the-art approaches. We make our code and data publicly available at https://github.com/lab-paper-code/FineCite."
}