Large-scale dataset of automatically classified rhetorical sections in scientific papers

2026-07-03

AI Summary

This study addresses the longstanding scarcity of large-scale, high-quality annotated data for automatic identification of rhetorical structures—such as Introduction, Methods, Results, and Discussion—in scientific papers. Leveraging the S2ORC corpus, the authors employ a rule-based classification algorithm to automatically annotate section-level rhetorical structures across 15.6 million STEM papers, yielding the first dataset of rhetorical annotations at a ten-million-paper scale. Rigorous evaluation through human assessment and validation by large language models demonstrates that the annotation quality closely aligns with manual labeling. The resulting dataset spans multiple disciplines, including medicine and biology, providing a high-quality resource for large-scale computational analysis of scientific writing patterns.

Abstract

Scientific papers follow rhetorical structures that organize content into sections such as Introduction, Methods, Results, and Discussion. Automatically identifying these sections at scale enables granular analysis of scientific writing patterns. We present a dataset of section-level annotations for millions of scientific papers from the Semantic Scholar Open Research Corpus (S2ORC). Using a rule-based classification algorithm, we identified and labeled major sections across 15.6 million papers after quality filtering. The dataset covers primarily STEM disciplines, with strong representation in medicine and biology. We provide comprehensive human and LLM-based validation showing that classifier agreement with human annotators is on par with human inter-annotator agreement. This dataset enables large-scale computational studies of scientific discourse and writing patterns.

Problem

• scientific papers
• rhetorical sections
• section classification
• scientific writing
• large-scale dataset

Innovation

• rhetorical section classification
• large-scale dataset
• scientific discourse analysis
• rule-based algorithm
• S2ORC