  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.

Computation and Language