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

2021-03-19 17:27:58
Nicholas Egan, Oleg Vasilyev, John Bohannon

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.

Abstract (translated)

URL

https://arxiv.org/abs/2103.10918

PDF

https://arxiv.org/pdf/2103.10918.pdf


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