Abstract
We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18- 39%. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.- Anthology ID:
- 2020.acl-main.124
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1347–1354
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.124
- DOI:
- 10.18653/v1/2020.acl-main.124
- Cite (ACL):
- Yang Gao, Wei Zhao, and Steffen Eger. 2020. SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1347–1354, Online. Association for Computational Linguistics.
- Cite (Informal):
- SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization (Gao et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.124.pdf
- Code
- yg211/acl20-ref-free-eval