Unsupervised Aspect-Based Multi-Document Abstractive Summarization

Maximin Coavoux, Hady Elsahar, Matthias Gallé


Abstract
User-generated reviews of products or services provide valuable information to customers. However, it is often impossible to read each of the potentially thousands of reviews: it would therefore save valuable time to provide short summaries of their contents. We address opinion summarization, a multi-document summarization task, with an unsupervised abstractive summarization neural system. Our system is based on (i) a language model that is meant to encode reviews to a vector space, and to generate fluent sentences from the same vector space (ii) a clustering step that groups together reviews about the same aspects and allows the system to generate summary sentences focused on these aspects. Our experiments on the Oposum dataset empirically show the importance of the clustering step.
Anthology ID:
D19-5405
Volume:
Proceedings of the 2nd Workshop on New Frontiers in Summarization
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
42–47
Language:
URL:
https://aclanthology.org/D19-5405
DOI:
10.18653/v1/D19-5405
Bibkey:
Cite (ACL):
Maximin Coavoux, Hady Elsahar, and Matthias Gallé. 2019. Unsupervised Aspect-Based Multi-Document Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 42–47, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Aspect-Based Multi-Document Abstractive Summarization (Coavoux et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-5405.pdf