---
title: "Towards Effective Extraction and Evaluation of Factual Claims"
description: "The authors propose an evaluation framework with three criteria: Entailment: claims must follow from… 51 references, cited by 14 papers here."
authors: ["Dasha Metropolitansky", "Jonathan Larson"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.348/"
doi: "10.18653/v1/2025.acl-long.348"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/metropolitansky-2025-effective-extraction-evaluation-factual-claims/"
---
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# Towards Effective Extraction and Evaluation of Factual Claims

**Authors:** Dasha Metropolitansky and Jonathan Larson · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.348/) · [PDF](https://aclanthology.org/2025.acl-long.348.pdf) · [DOI 10.18653/v1/2025.acl-long.348](https://doi.org/10.18653/v1/2025.acl-long.348) · [OpenAlex](https://openalex.org/W4412945668)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/metropolitansky-2025-effective-extraction-evaluation-factual-claims/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
A common way to fact-check long LLM outputs is to break the text into simple claims and verify each one separately. Wrong or missing claims distort the final verdicts, but there has been no shared way to judge how well claim extraction works.

The authors propose an evaluation framework with three criteria:
- **Entailment:** claims must follow from the source.
- **Coverage:** claims should capture verifiable content and leave out unverifiable content. Coverage is scored per sentence and also per "element", meaning each separate piece of information in a sentence.
- **Decontextualization:** judged by outcome. Both a claim and a fully contextualized version of it are fact-checked, and the method checks whether the verdicts agree.

All measurements are automated and were checked against human annotation.

The paper also presents Claimify, an LLM-based extractor. It detects sentences with several plausible readings and declines to extract claims when context cannot settle the intended one.

Tested on BingCheck with three LLMs, Claimify outperformed existing extractors. This makes it a more reliable first stage for fact-checking pipelines.

## Key points
- With mistral-large-2411, gpt-4o-2024-08-06 and DeepSeek-V3, at least 95% of Claimify's claims were entailed. It was best outright with Mistral and tied with one baseline on the other two models.
- Claimify had the highest accuracy and macro F1 for both sentence- and element-level coverage. It was also least likely to drop context that changes a factuality verdict.
- Element-level coverage counts explicit inclusion of unverifiable content as a false positive. Implicit inclusion is not penalized.
- The decontextualization test compares evidence and verdicts for a claim c and an expanded version c_max, which yields seven possible outcomes.
- Claimify was compared with Factcheck-GPT, SAFE, VeriScore, AFaCTA and DnD. Evaluation used a single dataset (BingCheck) and three human annotators. Missing temporal context remains an open problem.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Dasha Metropolitansky and Jonathan Larson. 2025. Towards Effective Extraction and Evaluation of Factual Claims. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{metropolitansky2025effective,
  title = {{Towards Effective Extraction and Evaluation of Factual Claims}},
  author = {Dasha Metropolitansky and Jonathan Larson},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.348},
  url = {https://doi.org/10.18653/v1/2025.acl-long.348}
}
```

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*Updated 31 October 2026*