Abstract
We present a generic method to compute thefactual accuracy of a generated data summarywith minimal user effort. We look at the prob-lem as a fact-checking task to verify the nu-merical claims in the text. The verification al-gorithm assumes that the data used to generatethe text is available. In this paper, we describehow the proposed solution has been used toidentify incorrect claims about basketball tex-tual summaries in the context of the AccuracyShared Task at INLG 2021.- Anthology ID:
- 2021.inlg-1.27
- Volume:
- Proceedings of the 14th International Conference on Natural Language Generation
- Month:
- August
- Year:
- 2021
- Address:
- Aberdeen, Scotland, UK
- Editors:
- Anya Belz, Angela Fan, Ehud Reiter, Yaji Sripada
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 271–275
- Language:
- URL:
- https://aclanthology.org/2021.inlg-1.27
- DOI:
- 10.18653/v1/2021.inlg-1.27
- Cite (ACL):
- Rayhane Rezgui, Mohammed Saeed, and Paolo Papotti. 2021. Automatic Verification of Data Summaries. In Proceedings of the 14th International Conference on Natural Language Generation, pages 271–275, Aberdeen, Scotland, UK. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Verification of Data Summaries (Rezgui et al., INLG 2021)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2021.inlg-1.27.pdf