Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science
Summary
This paper proposes a segment combination strategy for automatically classifying research methods in academic papers by partitioning full-text content. Experiments on an annotated corpus from Library and Information Science journals show that methodological information is unevenly distributed, with middle-to-late segments having higher discriminative power.
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# Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science Source: [https://arxiv.org/abs/2606.19051](https://arxiv.org/abs/2606.19051) [View PDF](https://arxiv.org/pdf/2606.19051) > Abstract:Research methods are essential carriers of knowledge contribution in academic papers\. Automatic multi\-label classification of research methods can support knowledge services such as method retrieval, review generation, and research intelligence analysis\. While existing studies primarily rely on titles and abstracts, abstracts often provide only limited methodological information, whereas utilizing full\-text content faces challenges related to excessive length and information redundancy\. Therefore, this paper proposes a segment combination strategy by partitioning the full\-text content according to its physical postion\. Using an annotated corpus of 1,954 full\-text articles from three representative journals in Library and Information Science \(JASIST, LISR, and JDoc\), we evaluate the classification performance of various segments and their combinations across multiple models\. Experimental results indicate that methodological information is distributed unevenly within the full\-text content, with the middle\-to\-late and final segments exhibiting greater discriminative power\. Furthermore, integrating bibliographic metadata with cross\-segment combination strategies effectively enhances classification performance\. ## Submission history From: Chengzhi Zhang \[[view email](https://arxiv.org/show-email/8619b055/2606.19051)\] **\[v1\]**Wed, 17 Jun 2026 13:17:41 UTC \(1,620 KB\)
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