Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation

• URL: http://arxiv.org/abs/2103.10918v1
• Date: Fri, 19 Mar 2021 17:27:58 GMT
• Title: Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation
• Authors: Nicholas Egan, Oleg Vasilyev, John Bohannon
• Abstract summary: We introduce new summary evaluation metrics that use a pretrained language model to estimate the information shared between a document and its summary. These metrics are a modern take on the Shannon Game, a method for summary quality scoring proposed decades ago. We empirically verify that the introduced metrics correlate with human judgement based on coverage, overall quality, and five summary dimensions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of a summary is to concisely state the most important information in a document. With this principle in mind, we introduce new reference-free summary evaluation metrics that use a pretrained language model to estimate the information shared between a document and its summary. These metrics are a modern take on the Shannon Game, a method for summary quality scoring proposed decades ago, where we replace human annotators with language models. We also view these metrics as an extension of BLANC, a recently proposed approach to summary quality measurement based on the performance of a language model with and without the help of a summary. Using GPT-2, we empirically verify that the introduced metrics correlate with human judgement based on coverage, overall quality, and five summary dimensions.

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