OpinionDigest: A Simple Framework for Opinion Summarization

Yoshihiko Suhara, Xiaolan Wang, Stefanos Angelidis, Wang-Chiew Tan


Abstract
We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment. Automatic evaluation on Yelp data shows that our framework outperforms competitive baselines. Human studies on two corpora verify that OpinionDigest produces informative summaries and shows promising customization capabilities.
Anthology ID:
2020.acl-main.513
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:
5789–5798
Language:
URL:
https://aclanthology.org/2020.acl-main.513
DOI:
10.18653/v1/2020.acl-main.513
Bibkey:
Cite (ACL):
Yoshihiko Suhara, Xiaolan Wang, Stefanos Angelidis, and Wang-Chiew Tan. 2020. OpinionDigest: A Simple Framework for Opinion Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5789–5798, Online. Association for Computational Linguistics.
Cite (Informal):
OpinionDigest: A Simple Framework for Opinion Summarization (Suhara et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.513.pdf
Video:
 http://slideslive.com/38929096
Code
 megagonlabs/opiniondigest