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        <title>Unsupervised Extractive Summarization using Pointwise Mutual Information</title>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.213</identifier>
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%0 Conference Proceedings
%T Unsupervised Extractive Summarization using Pointwise Mutual Information
%A Padmakumar, Vishakh
%A He, He
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F padmakumar-he-2021-unsupervised
%X 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.
%R 10.18653/v1/2021.eacl-main.213
%U https://aclanthology.org/2021.eacl-main.213
%U https://doi.org/10.18653/v1/2021.eacl-main.213
%P 2505-2512

Markdown (Informal)

[Unsupervised Extractive Summarization using Pointwise Mutual Information](https://aclanthology.org/2021.eacl-main.213) (Padmakumar & He, EACL 2021)

• Unsupervised Extractive Summarization using Pointwise Mutual Information (Padmakumar & He, EACL 2021)

ACL

• Vishakh Padmakumar and He He. 2021. Unsupervised Extractive Summarization using Pointwise Mutual Information. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2505–2512, Online. Association for Computational Linguistics.