# Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning

arXiv: https://arxiv.org/abs/1907.00962 · HTML: https://mlsanity.com/paper/1907.00962/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets](https://mlsanity.com/paper/2209.07834/) (2022)
- [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)
- [Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature](https://mlsanity.com/paper/2212.09867/) (2022)
- [PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A](https://mlsanity.com/paper/2602.21045/) (2026)
- [ClaimFlow: Tracing the Evolution of Scientific Claims in NLP](https://mlsanity.com/paper/2603.16073/) (2026)
- [Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information](https://mlsanity.com/paper/2305.11553/) (2023)
- [What Are Research Hypotheses?](https://mlsanity.com/paper/2509.00185/) (2025)
- [Context Selection for Hypothesis and Statistical Evidence Extraction from Full-Text Scientific Articles](https://mlsanity.com/paper/2603.21193/) (2026)
- [Can Scientific Claims Be Removed from Large Language Models? A Systematic Evaluation of Claim-Level Unlearning](https://mlsanity.com/paper/2608.20960/) (2026)

## Abstract (arXiv)

Claims are a fundamental unit of scientific discourse. The exponential growth in the number of scientific publications makes automatic claim extraction an important problem for researchers who are overwhelmed by this information overload. Such an automated claim extraction system is useful for both manual and programmatic exploration of scientific knowledge. In this paper, we introduce a new dataset of 1,500 scientific abstracts from the biomedical domain with expert annotations for each sentence indicating whether the sentence presents a scientific claim. We introduce a new model for claim extraction and compare it to several baseline models including rule-based and deep learning techniques. Moreover, we show that using a transfer learning approach with a fine-tuning step allows us to improve performance from a large discourse-annotated dataset. Our final model increases F1-score by over 14 percent points compared to a baseline model without transfer learning. We release a publicly accessible tool for discourse and claims prediction along with an annotation tool. We discuss further applications beyond biomedical literature.