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

• URL: http://arxiv.org/abs/2005.03724v1
• Date: Thu, 7 May 2020 19:54:24 GMT
• Title: SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
• Authors: Yang Gao, Wei Zhao, Steffen Eger
• Abstract summary: We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18-39%. 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.
• Score: 31.082618343998533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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