# Scientific Discourse Tagging for Evidence Extraction

arXiv: https://arxiv.org/abs/1909.04758 · HTML: https://mlsanity.com/paper/1909.04758/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 11 of 11)

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- [Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets](https://mlsanity.com/paper/2209.07834/) (2022)
- [NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment](https://mlsanity.com/paper/2604.11543/) (2026)
- [Full-Text Argumentation Mining on Scientific Publications](https://mlsanity.com/paper/2210.13084/) (2022)
- [Citation Recommendation based on Argumentative Zoning of User Queries](https://mlsanity.com/paper/2501.18292/) (2025)
- [Multi-label Sequential Sentence Classification via Large Language Model](https://mlsanity.com/paper/2411.15623/) (2024)
- ['Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism](https://mlsanity.com/paper/2310.15077/) (2023)
- [Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs](https://mlsanity.com/paper/2510.25370/) (2025)

## Abstract (arXiv)

Evidence plays a crucial role in any biomedical research narrative, providing justification for some claims and refutation for others. We seek to build models of scientific argument using information extraction methods from full-text papers. We present the capability of automatically extracting text fragments from primary research papers that describe the evidence presented in that paper's figures, which arguably provides the raw material of any scientific argument made within the paper. We apply richly contextualized deep representation learning pre-trained on biomedical domain corpus to the analysis of scientific discourse structures and the extraction of "evidence fragments" (i.e., the text in the results section describing data presented in a specified subfigure) from a set of biomedical experimental research articles. We first demonstrate our state-of-the-art scientific discourse tagger on two scientific discourse tagging datasets and its transferability to new datasets. We then show the benefit of leveraging scientific discourse tags for downstream tasks such as claim-extraction and evidence fragment detection. Our work demonstrates the potential of using evidence fragments derived from figure spans for improving the quality of scientific claims by cataloging, indexing and reusing evidence fragments as independent documents.