SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization

Yang Gao, Wei Zhao, Steffen Eger


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.124.pdf
Video:
 http://slideslive.com/38929051
Code
 yg211/acl20-ref-free-eval