@inproceedings{eldifrawi-etal-2025-fingract,
    title = "{F}in{G}r{A}ct: A Framework for {FIN}e-{GR}rained Evaluation of {ACT}ionability in Explainable Automatic Fact-Checking",
    author = "Eldifrawi, Islam  and
      Wang, Shengrui  and
      Trabelsi, Amine",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.525/",
    doi = "10.18653/v1/2025.findings-emnlp.525",
    pages = "9882--9901",
    ISBN = "979-8-89176-335-7",
    abstract = "The field of explainable Automatic Fact-Checking (AFC) aims to enhance the transparency and trustworthiness of automated fact verification systems by providing clear and comprehensible explanations. However, the effectiveness of these explanations depends ontheir actionability{---}the extent to which an AFC explanation pinpoints the error, supplies the correct fact, and backs it with sources. Despiteactionability being critical for high-quality explanations, no prior research has proposed a method to evaluate it. This paper introducesFinGrAct, a fine-grained evaluation framework that can access the web and is designed to assess actionability in AFC explanations through well-defined criteria. We also introduce a novel dataset to evaluate actionability in AFC explanations. FinGrAct surpasses state-of-the-art (SOTA) evaluators, achieving the highest Pearson and Kendall correlation with human judgments while demonstrating the lowest egocentricbias, making it a more robust evaluation approach for actionability evaluation in AFC."
}