Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, Shouling Ji
Abstract
Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the informativeness of the assessed summary and require human-generated references for each test summary. In this work, we propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. To learn the metric, for each summary, we construct different types of negative samples with respect to different aspects of the summary qualities, and train our model with a ranking loss. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries. Furthermore, we show that our method is general and transferable across datasets.- Anthology ID:
- 2020.emnlp-main.294
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3612–3621
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.294
- DOI:
- 10.18653/v1/2020.emnlp-main.294
- Cite (ACL):
- Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, and Shouling Ji. 2020. Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3612–3621, Online. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning (Wu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.emnlp-main.294.pdf
- Code
- whl97/LS-Score
- Data
- CNN/Daily Mail, NEWSROOM