Unsupervised Extractive Summarization using Pointwise Mutual Information

Vishakh Padmakumar; He He

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arXiv · 2102.06272

Unsupervised Extractive Summarization using Pointwise Mutual Information

Vishakh Padmakumar·He He

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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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Vishakh Padmakumar, He He. 2021-02-11. Unsupervised Extractive Summarization using Pointwise Mutual Information. https://arxiv.org/abs/2102.06272

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