Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science
- URL: http://arxiv.org/abs/2606.19051v1
- Date: Wed, 17 Jun 2026 13:17:41 GMT
- Title: Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science
- Abstract summary: This paper proposes a segment combination strategy by partitioning the full-text content according to its physical postion.<n>Using an annotated corpus of 1,954 full-text articles from three representative journals in Library and Information Science, we evaluate the classification performance of various segments.
- Score: 2.8993790400286876
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Large-Scale Multidimensional Knowledge Profiling of Scientific Literature [46.15403461273178]
We compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025.<n>Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies.<n>These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
arXiv Detail & Related papers (2026-01-21T16:47:05Z) - Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering [59.54662810933882]
Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models, often lack coherence and granularity.<n>We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering.
arXiv Detail & Related papers (2025-09-23T15:12:58Z) - A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora [6.384357773998868]
Sci-OG is a semi-auto-mated methodology for generating research topic.<n>This paper presents Sci-OG, a semi-auto-mated methodology for generating research topic.<n>We evaluate this approach against a range of alternative solutions using a dataset of 21,649 manually annotated semantic triples.
arXiv Detail & Related papers (2025-08-06T08:48:14Z) - Enhancing Abstractive Summarization of Scientific Papers Using Structure Information [6.414732533433283]
We propose a two-stage abstractive summarization framework that leverages automatic recognition of structural functions within scientific papers.<n>In the first stage, we standardize chapter titles from numerous scientific papers and construct a large-scale dataset for structural function recognition.<n>In the second stage, we employ Longformer to capture rich contextual relationships across sections and generating context-aware summaries.
arXiv Detail & Related papers (2025-05-20T10:34:45Z) - An Instance-based Plus Ensemble Learning Method for Classification of Scientific Papers [2.0794749869068005]
This paper introduces a novel approach that combines instance-based learning and ensemble learning techniques for classifying scientific papers.
Experiments show that the proposed classification method is effective and efficient in categorizing papers into various research areas.
arXiv Detail & Related papers (2024-09-21T19:42:15Z) - Bridging Research and Readers: A Multi-Modal Automated Academic Papers
Interpretation System [47.13932723910289]
We introduce an open-source multi-modal automated academic paper interpretation system (MMAPIS) with three-step process stages.
It employs the hybrid modality preprocessing and alignment module to extract plain text, and tables or figures from documents separately.
It then aligns this information based on the section names they belong to, ensuring that data with identical section names are categorized under the same section.
It utilizes the extracted section names to divide the article into shorter text segments, facilitating specific summarizations both within and between sections via LLMs.
arXiv Detail & Related papers (2024-01-17T11:50:53Z) - A Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights [2.1436706159840013]
This survey paper introduces a comprehensive taxonomy specifically designed for text classification based on research fields.
The taxonomy is structured into hierarchical levels: research field-based category, research field-based sub-category, methodology-based technique, methodology sub-technique, and research field applications.
arXiv Detail & Related papers (2024-01-11T08:17:42Z) - Incremental hierarchical text clustering methods: a review [49.32130498861987]
This study aims to analyze various hierarchical and incremental clustering techniques.
The main contribution of this research is the organization and comparison of the techniques used by studies published between 2010 and 2018 that aimed to texts documents clustering.
arXiv Detail & Related papers (2023-12-12T22:27:29Z) - MORTY: Structured Summarization for Targeted Information Extraction from
Scholarly Articles [0.0]
We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles.
Our approach condenses the article's full-text to property-value pairs as a segmented text snippet called structured summary.
We also present a sizable scholarly dataset combining structured summaries retrieved from a scholarly knowledge graph and corresponding publicly available scientific articles.
arXiv Detail & Related papers (2022-12-11T06:49:29Z) - CitationIE: Leveraging the Citation Graph for Scientific Information
Extraction [89.33938657493765]
We use the citation graph of referential links between citing and cited papers.
We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
arXiv Detail & Related papers (2021-06-03T03:00:12Z) - A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021.
We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
arXiv Detail & Related papers (2020-08-02T00:09:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.