Aspect-Controllable Opinion Summarization

Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata


Abstract
Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an approach that allows the generation of customized summaries based on aspect queries (e.g., describing the location and room of a hotel). Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers. Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.
Anthology ID:
2021.emnlp-main.528
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6578–6593
Language:
URL:
https://aclanthology.org/2021.emnlp-main.528
DOI:
10.18653/v1/2021.emnlp-main.528
Bibkey:
Cite (ACL):
Reinald Kim Amplayo, Stefanos Angelidis, and Mirella Lapata. 2021. Aspect-Controllable Opinion Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6578–6593, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Aspect-Controllable Opinion Summarization (Amplayo et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.528.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.528.mp4
Code
 rktamplayo/acesum