Unsupervised Extractive Summarization using Pointwise Mutual Information

• URL: http://arxiv.org/abs/2102.06272v1
• Date: Thu, 11 Feb 2021 21:05:50 GMT
• Title: Unsupervised Extractive Summarization using Pointwise Mutual Information
• Authors: Vishakh Padmakumar, He He
• Abstract summary: We propose new metrics of relevance and redundancy using pointwise mutual information (PMI) between sentences. We show that our method outperforms similarity-based methods on datasets in a range of domains including news, medical journal articles, and personal anecdotes.
• Score: 5.544401446569243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document. We propose new metrics of relevance and redundancy using pointwise mutual information (PMI) between sentences, which can be easily computed by a pre-trained language model. Intuitively, a relevant sentence allows readers to infer the document content (high PMI with the document), and a redundant sentence can be inferred from the summary (high PMI with the summary). We then develop a greedy sentence selection algorithm to maximize relevance and minimize redundancy of extracted sentences. We show that our method outperforms similarity-based methods on datasets in a range of domains including news, medical journal articles, and personal anecdotes.

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