Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov


Abstract
Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.
Anthology ID:
2021.naacl-main.383
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4812–4829
Language:
URL:
https://aclanthology.org/2021.naacl-main.383
DOI:
10.18653/v1/2021.naacl-main.383
Bibkey:
Cite (ACL):
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021. Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4812–4829, Online. Association for Computational Linguistics.
Cite (Informal):
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (Pagnoni et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.383.pdf
Video:
 https://aclanthology.org/2021.naacl-main.383.mp4
Code
 artidoro/frank