SAFE: Structured Argumentation for Fact-Checking with Explanations

Abstract

Explainable fact-checking plays a vital role in the fight against disinformation in today’s digital landscape. With the increasing volume of unverified content online, providing justifications for fact-checking has become essential to help users make informed decisions. While recent studies provide user-friendly explanations through abstractive or extractive summarization, they often assume the availability of human-written fact-checking articles, which is not always the case. This demo introduces SAFE, an argument-based framework designed to enhance both fact-checking and its justification. Specifically, SAFE offers three key features: i) producing argument-structured summaries of human-written fact-checking articles, ii) in the absence of human-written articles, generating structured summaries based on evidence retrieved from a corpus through a jointly trained summarization and evidence retrieval system, and iii) assessing the truthfulness of a claim by analyzing the structured summary.

Cite

Text

Wang et al. "SAFE: Structured Argumentation for Fact-Checking with Explanations." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1274

Markdown

[Wang et al. "SAFE: Structured Argumentation for Fact-Checking with Explanations." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/wang2025ijcai-safe/) doi:10.24963/IJCAI.2025/1274

BibTeX

@inproceedings{wang2025ijcai-safe,
  title     = {{SAFE: Structured Argumentation for Fact-Checking with Explanations}},
  author    = {Wang, Xiaoou and Cabrio, Elena and Villata, Serena},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {11114-11118},
  doi       = {10.24963/IJCAI.2025/1274},
  url       = {https://mlanthology.org/ijcai/2025/wang2025ijcai-safe/}
}