Unsupervised Extractive Opinion Summarization Using Sparse Coding

Somnath Basu Roy Chowdhury, Chao Zhao, Snigdha Chaturvedi


Abstract
Opinion summarization is the task of automatically generating summaries that encapsulate information expressed in multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from the review text and learns a latent representation of each sentence over semantic units. Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews. SemAE is also able to perform controllable summarization to generate aspect-specific summaries using only a few samples. We report strong performance on SPACE and AMAZON datasets and perform experiments to investigate the functioning of our model.
Anthology ID:
2022.acl-long.86
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1209–1225
Language:
URL:
https://aclanthology.org/2022.acl-long.86
DOI:
10.18653/v1/2022.acl-long.86
Bibkey:
Cite (ACL):
Somnath Basu Roy Chowdhury, Chao Zhao, and Snigdha Chaturvedi. 2022. Unsupervised Extractive Opinion Summarization Using Sparse Coding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1209–1225, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Extractive Opinion Summarization Using Sparse Coding (Basu Roy Chowdhury et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.86.pdf
Software:
 2022.acl-long.86.software.zip
Code
 brcsomnath/semae