Aspect-Controllable Opinion Summarization

Understand

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.