RT @vlachos_nlp: Pleased to announce the next @FEVERworkshop at ACL2025! Regular workshop papers (ARR and direct submissions) due 15th of A….

RT @vlachos_nlp: @MichaelSejr @c_christodoulop @chenxi_jw @j6mes organising the @FEVERworkshop and presenting the s….

RT @michael_sejr: Hi #NLProc folks 👋 Got a paper on #factchecking or a related task with ARR reviews? Submit it to @FEVERworkshop! Our comm….

RT @l2m2_workshop: 📣Hey #NLProc! We* are planning to organize a *ACL workshop on memorization in LLM. Goal: provide a central venue to disc….

RT @vlachos_nlp: Time to report on the AVERITEC shared task @FEVERworkshop ! We had 21 teams making participating (….

RT @vlachos_nlp: With the great team @akhtarmubashara @Ramiyaly Rui Cao @Yulongchen1010 @c_christodoulop Oana Cocarascu @ZhenyunDeng @Zhiji….

RT @vlachos_nlp: The call for papers for the 7th @FEVERworkshop at #EMNLP is out: Deadline for ARR and non-ARR pap….

(9/9) When evaluated for evidence retrieval potential, the decontextualised claims obtained by enriching original sentences with the necessary context, are better than the original claim sentences, with an average 1.08 improvement in precision.

(8/9) The results were verified further by a fact-checking professional, as the sentences returned by our method were deemed central to the document more often, and check-worthy more often than those extracted by Claimbuster.

(7/9) Our method achieves a Precision@1 score of 47.8 on identifying central sentences, a 10% improvement over Claimbuster. Our method achieved a chrf score of 26.4 against gold decontextualised claims, outperforming all baselines.

(6/9) To evaluate our method, we derive a claim extraction dataset containing decontextualised claims from AVeriTeC (, a recently proposed dataset containing claims from real-world fact-checking articles.

(5/9) ii) we decontextualise central sentences to be understandable out of context by enriching them with the necessary context; iii) we introduce a QA-based framework to obtain the necessary context by resolving ambiguous information units in the extracted sentence.

(4/9) Assuming that salient claims are derived from central sentences, i) we recast the document-level CE task into the extractive summarization task to extract central sentences and reduce redundancy;.

(3/9) We propose a novel method for document-level claim extraction and decontexualisation for fact-checking, aiming to extract salient check-worthy claims from documents that can be understood outside the context of the document.

(2/9) In real-world scenarios, Claims often need to be extracted from documents containing multiple claims, not all of which are relevant to the central idea of the document, and verifying all claims manually or even automatically would be inefficient.

(1/9) Existing claim extraction methods mainly focus on detecting whether a sentence contains a claim or the boundaries of the claim within a sentence.

Happy to share that our paper "Document-level Claim Extraction and Decontextualisation for Fact-Checking" has been to ACL 2024. Joint work with my collaborators @vlachos_nlp and @michael_sejr. arXiv: github:

RT @vlachos_nlp: Interested in fact-checking? Just announced: Next @FEVERworkshop at EMNLP shared task: aiming to….