From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917467755905024 |
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| author | Sahitaj, Premtim Kolanowski, Jawan Sahitaj, Ariana Solopova, Veronika Upravitelev, Max Röder, Daniel Maab, Iffat Yamagishi, Junichi Möller, Sebastian Schmitt, Vera |
| author_facet | Sahitaj, Premtim Kolanowski, Jawan Sahitaj, Ariana Solopova, Veronika Upravitelev, Max Röder, Daniel Maab, Iffat Yamagishi, Junichi Möller, Sebastian Schmitt, Vera |
| contents | Fact-checking articles encode rich supporting evidence and reasoning, yet this evidence remains largely inaccessible to automated verification systems due to unstructured presentation. We introduce PrimeFacts, a methodology and resource for extracting fine-grained evidence from full fact-checking articles. We compile 13,106 PolitiFact articles with claims, verdicts, and all referenced sources, and we identify 49,718 in-article hyperlinks as natural anchors to pinpoint key evidence. Our framework leverages large language models (LLMs) to rewrite these anchor sentences into stand-alone, context-independent premises and investigates the extraction of additional implicit evidence. In evaluations on cross-article evidence retrieval and claim verification, the extracted premises substantially improve performance. Decontextualized evidence yields higher retrievability, achieving up to a 30 percent relative gain in Mean Reciprocal Rank over verbatim sentences, and using the evidence for verdict prediction raises Macro-F1 by 10-20 points over the baseline. These gains are consistent across different verdict granularities (2-class vs. 5-class) and model architectures. A qualitative analysis indicates that the decontextualized premises remain faithful to the original sources. Our work highlights the promise of reusing fact-checkers' evidence for automation and provides a large-scale resource of structured evidence from real-world fact-checks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence Sahitaj, Premtim Kolanowski, Jawan Sahitaj, Ariana Solopova, Veronika Upravitelev, Max Röder, Daniel Maab, Iffat Yamagishi, Junichi Möller, Sebastian Schmitt, Vera Computation and Language Fact-checking articles encode rich supporting evidence and reasoning, yet this evidence remains largely inaccessible to automated verification systems due to unstructured presentation. We introduce PrimeFacts, a methodology and resource for extracting fine-grained evidence from full fact-checking articles. We compile 13,106 PolitiFact articles with claims, verdicts, and all referenced sources, and we identify 49,718 in-article hyperlinks as natural anchors to pinpoint key evidence. Our framework leverages large language models (LLMs) to rewrite these anchor sentences into stand-alone, context-independent premises and investigates the extraction of additional implicit evidence. In evaluations on cross-article evidence retrieval and claim verification, the extracted premises substantially improve performance. Decontextualized evidence yields higher retrievability, achieving up to a 30 percent relative gain in Mean Reciprocal Rank over verbatim sentences, and using the evidence for verdict prediction raises Macro-F1 by 10-20 points over the baseline. These gains are consistent across different verdict granularities (2-class vs. 5-class) and model architectures. A qualitative analysis indicates that the decontextualized premises remain faithful to the original sources. Our work highlights the promise of reusing fact-checkers' evidence for automation and provides a large-scale resource of structured evidence from real-world fact-checks. |
| title | From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.06006 |