  Opinion summarization is the automatic creation of text reflecting subjective
information expressed in multiple documents, such as user reviews of a product.
The task is practically important and has attracted a lot of attention.
However, due to the high cost of summary production, datasets large enough for
training supervised models are lacking. Instead, the task has been
traditionally approached with extractive methods that learn to select text
fragments in an unsupervised or weakly-supervised way. Recently, it has been
shown that abstractive summaries, potentially more fluent and better at
reflecting conflicting information, can also be produced in an unsupervised
fashion. However, these models, not being exposed to actual summaries, fail to
capture their essential properties. In this work, we show that even a handful
of summaries is sufficient to bootstrap generation of the summary text with all
expected properties, such as writing style, informativeness, fluency, and
sentiment preservation. We start by training a conditional Transformer language
model to generate a new product review given other available reviews of the
product. The model is also conditioned on review properties that are directly
related to summaries; the properties are derived from reviews with no manual
effort. In the second stage, we fine-tune a plug-in module that learns to
predict property values on a handful of summaries. This lets us switch the
generator to the summarization mode. We show on Amazon and Yelp datasets that
our approach substantially outperforms previous extractive and abstractive
methods in automatic and human evaluation.
