  We present a neural framework for opinion summarization from online product
reviews which is knowledge-lean and only requires light supervision (e.g., in
the form of product domain labels and user-provided ratings). Our method
combines two weakly supervised components to identify salient opinions and form
extractive summaries from multiple reviews: an aspect extractor trained under a
multi-task objective, and a sentiment predictor based on multiple instance
learning. We introduce an opinion summarization dataset that includes a
training set of product reviews from six diverse domains and human-annotated
development and test sets with gold standard aspect annotations, salience
labels, and opinion summaries. Automatic evaluation shows significant
improvements over baselines, and a large-scale study indicates that our opinion
summaries are preferred by human judges according to multiple criteria.
