Unsupervised Opinion Summarization with Noising and Denoising

Reinald Kim Amplayo, Mirella Lapata


Abstract
The supervised training of high-capacity models on large datasets containing hundreds of thousands of document-summary pairs is critical to the recent success of deep learning techniques for abstractive summarization. Unfortunately, in most domains (other than news) such training data is not available and cannot be easily sourced. In this paper we enable the use of supervised learning for the setting where there are only documents available (e.g., product or business reviews) without ground truth summaries. We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input. We introduce several linguistically motivated noise generation functions and a summarization model which learns to denoise the input and generate the original review. At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise. Extensive automatic and human evaluation shows that our model brings substantial improvements over both abstractive and extractive baselines.
Anthology ID:
2020.acl-main.175
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1934–1945
Language:
URL:
https://aclanthology.org/2020.acl-main.175
DOI:
10.18653/v1/2020.acl-main.175
Bibkey:
Cite (ACL):
Reinald Kim Amplayo and Mirella Lapata. 2020. Unsupervised Opinion Summarization with Noising and Denoising. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1934–1945, Online. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Opinion Summarization with Noising and Denoising (Amplayo & Lapata, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.175.pdf
Video:
 http://slideslive.com/38928750
Code
 rktamplayo/DenoiseSum