  Opinion summarization has been traditionally approached with unsupervised,
weakly-supervised and few-shot learning techniques. In this work, we collect a
large dataset of summaries paired with user reviews for over 31,000 products,
enabling supervised training. However, the number of reviews per product is
large (320 on average), making summarization - and especially training a
summarizer - impractical. Moreover, the content of many reviews is not
reflected in the human-written summaries, and, thus, the summarizer trained on
random review subsets hallucinates. In order to deal with both of these
challenges, we formulate the task as jointly learning to select informative
subsets of reviews and summarizing the opinions expressed in these subsets. The
choice of the review subset is treated as a latent variable, predicted by a
small and simple selector. The subset is then fed into a more powerful
summarizer. For joint training, we use amortized variational inference and
policy gradient methods. Our experiments demonstrate the importance of
selecting informative reviews resulting in improved quality of summaries and
reduced hallucinations.
