Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
I-chun Chern, Zhiruo Wang, Sanjan Das, Bhavuk Sharma, Pengfei Liu, Graham Neubig
Abstract
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries. Code and human evaluation results will be publicly available at \url{https://github.com/EthanC111/factuality_summarization}.- Anthology ID:
- 2023.trustnlp-1.6
- Volume:
- Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anaelia Ovalle, Kai-Wei Chang, Ninareh Mehrabi, Yada Pruksachatkun, Aram Galystan, Jwala Dhamala, Apurv Verma, Trista Cao, Anoop Kumar, Rahul Gupta
- Venue:
- TrustNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 55–60
- Language:
- URL:
- https://aclanthology.org/2023.trustnlp-1.6
- DOI:
- 10.18653/v1/2023.trustnlp-1.6
- Cite (ACL):
- I-chun Chern, Zhiruo Wang, Sanjan Das, Bhavuk Sharma, Pengfei Liu, and Graham Neubig. 2023. Improving Factuality of Abstractive Summarization via Contrastive Reward Learning. In Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023), pages 55–60, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Improving Factuality of Abstractive Summarization via Contrastive Reward Learning (Chern et al., TrustNLP 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.trustnlp-1.6.pdf