Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News
Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Rada Mihalcea
Abstract
In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications. We experiment with two methods: (1) an extractive method based on Biased TextRank – a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model. We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.- Anthology ID:
- 2021.nlp4if-1.7
- Volume:
- Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Anna Feldman, Giovanni Da San Martino, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 45–50
- Language:
- URL:
- https://aclanthology.org/2021.nlp4if-1.7
- DOI:
- 10.18653/v1/2021.nlp4if-1.7
- Cite (ACL):
- Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, and Rada Mihalcea. 2021. Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 45–50, Online. Association for Computational Linguistics.
- Cite (Informal):
- Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News (Kazemi et al., NLP4IF 2021)
- PDF:
- https://preview.aclanthology.org/cschoel_rss_and_blog/2021.nlp4if-1.7.pdf