---
title: "SAFE: Structured Argumentation for Fact-checking with Explanations"
authors:
  - "Xiaoou Wang"
  - "Elena Cabrio"
  - "Serena Villata"
doi: "10.24963/ijcai.2025/1274"
url: "https://ijcai.org/proceedings/2025/1274"
pdf: "https://ijcai.org/proceedings/2025/1274.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "11114-11118"
track: "Demo Track"
keywords:
  - "Natural Language Processing: NLP: Sentiment analysis, stylistic analysis, and argument mining"
  - "Natural Language Processing: NLP: Applications"
---

# SAFE: Structured Argumentation for Fact-checking with Explanations

Xiaoou Wang, Elena Cabrio, Serena Villata

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 11114-11118. DOI: [10.24963/ijcai.2025/1274](https://doi.org/10.24963/ijcai.2025/1274)

## 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.