Informative and Controllable Opinion Summarization
Reinald Kim Amplayo, Mirella Lapata

TL;DR
This paper introduces a new opinion summarization framework that uses dense representations of reviews to generate more informative and customizable summaries, overcoming limitations of extractive methods.
Contribution
The proposed framework allows the use of all reviews without pre-selection and incorporates user preferences through zero-shot customization, improving summary informativeness.
Findings
Outperforms state-of-the-art on Rotten Tomatoes dataset
Enables effective user preference customization
Produces more informative summaries
Abstract
Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries. The framework enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model. We showcase an…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
