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.- Anthology ID:
- 2021.eacl-main.213
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2505–2512
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.213
- DOI:
- 10.18653/v1/2021.eacl-main.213
- Cite (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.
- Cite (Informal):
- Unsupervised Extractive Summarization using Pointwise Mutual Information (Padmakumar & He, EACL 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.eacl-main.213.pdf
- Code
- vishakhpk/mi-unsup-summ
- Data
- Reddit TIFU