F ine C ite: A Novel Approach For Fine-Grained Citation Context Analysis (2025)

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.

## introduction scientific research is inherently collaborative, with each discovery building upon a foundation of prior studies. to acknowledge previous work and provide credit, it is standard practice to include citations that connect past findings to new contributions. by embedding scientific progress and argumentation, citations serve a critical function that has been extensively examined-a research field known as citation context analysis (cca) @xcite . in computational linguistics, cca is mainly concerned with the automatic classification of citations along various dimensions, such as citation function @xcite @xcite , sentiment @xcite @xcite , or influence @xcite . given a passage of text surrounding a citation marker-referred to as the citation context-one or more classes from a predefined citation classification scheme are assigned. although a considerable amount of research has explored different classification schemes and methods, the citation context has received relatively little attention. this lack of focus has led to an absence of a comprehensive definition and datasets with overly simplistic and coarse-grained citation contexts @xcite . to address this concern, we analyze different context conceptualizations in previous work and provide a new comprehensive definition based on the semantic information of the citing text. a visual comparison is provided in figure 1 . with our research and artifacts, we hope to spark new interest in the exploration of citation context information. given the drastic capabilities of llms in zero-shot text understanding @xcite @xcite , and the emergence of advanced language processing systems @xcite , we argue that an improved understanding of contextual citation information is essential for improving interactive exploration of scientific argumentation. our contributions are the following: the rest of the paper is organized as follows. the subsequent section reviews relevant literature in the field of cca and provides a formalization of task and context conceptualization. section 3 introduces our citation context definition. in section 4, we describe the curation process of the finecite corpus and provide core statistics. in section 5, we assess the effectiveness of our context definition in both context extraction and citation classification. section 6 summarizes our contributions and outlines directions for future research. despite the continued research in cca, the introduction of new and larger datasets @xcite , and updated methodology @xcite @xcite , the simple modeling paradigm as described in equation 1 prevailed. the popular scicite benchmark @xcite even further simplifies the task by reducing the commonly used six-class framework of @xcite to a three-class schema. this simplicity leads to a low task complexity; however, it often fails to adequately represent the rich information present in the scientific texts @xcite . to capture a wider range of information, it is necessary to move beyond prevalent context span constraints and conceptualization on mutually exclusive classes. ## fine-grained citation context in this section, we propose and formalize our fine-grained context definition. ## finecite corpus using the definition in section 3, we create the finecite corpus. with the dataset creation, we aim to (i) assess whether the theoretical framework practically applies to scientific texts, (ii) investigate the assumption on the semantic dimensions and structure of citation contexts, and (iii) create a resource for the evaluation of the framework on established cca benchmarks. ## experiments in this section, we evaluate the finecite dataset on two tasks: (i) extraction of fine-grained citation contexts, and (ii) citation classification on standard cca benchmarks using fine-grained context information extracted in (i). ## percentage points 6 conclusion in this paper, we introduced a novel approach to defining citation contexts, aiming to foster new research in citation context analysis. we proposed a conceptual framework that characterizes citation context based on semantic dimensions and structural properties. subsequently, we described the curation of the finecite corpus-a first resource for fine-grained citation contexts-and analyzed core statistics. our experiments demonstrated that our context definition is practically applicable and leads to improved performance on established cca benchmarks compared to state-of-the-art methods. in future work, we will focus on expanding the dataset to a wider range of scientific texts and domains and further refining our context definition. additionally, we plan to explore applications, such as retrieval-augmented generation (rag) @xcite and question-answering (q&a) frameworks @xcite , to support interactive exploration of scientific argumentation.