Aspect-Controllable Opinion Summarization
This work addresses the need for aspect-controllable summarization in domains like reviews, offering a novel approach for generating tailored summaries, though it is incremental in building on existing opinion summarization techniques.
The paper tackles the problem of generating customized opinion summaries based on specific aspect queries, such as hotel location and room, by creating a synthetic training dataset and fine-tuning a pretrained model. The result shows that their model outperforms previous state-of-the-art methods on two benchmarks, enabling personalized summaries with controlled aspects.
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.