Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science

2026-06-17

AI Summary

This study addresses the limitations of existing automatic multi-label classification approaches for scholarly papers, which predominantly rely on titles and abstracts—often insufficient for accurate labeling—while full texts are lengthy and exhibit uneven information distribution. To overcome this, the authors propose segmenting full-text articles by physical position and systematically evaluating the discriminative power of individual sections and their combinations. Their analysis reveals that middle-to-late and concluding sections carry higher informational value. By integrating these informative segments with bibliographic metadata, they construct an enhanced multi-label classification model. Experiments on a corpus of 1,954 library and information science journal articles demonstrate that the proposed cross-segment combination and metadata fusion strategy significantly improves classification accuracy, offering a novel and effective pathway for fine-grained method identification in academic texts.

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.

Problem

• research methods
• automatic classification
• full-text analysis
• segmentation
• knowledge extraction

Innovation

• segment combination strategy
• research method classification
• full-text analysis
• multi-label classification
• bibliographic metadata integration