  Previous work on review summarization focused on measuring the sentiment
toward the main aspects of the reviewed product or business, or on creating a
textual summary. These approaches provide only a partial view of the data:
aspect-based sentiment summaries lack sufficient explanation or justification
for the aspect rating, while textual summaries do not quantify the significance
of each element, and are not well-suited for representing conflicting views.
Recently, Key Point Analysis (KPA) has been proposed as a summarization
framework that provides both textual and quantitative summary of the main
points in the data. We adapt KPA to review data by introducing Collective Key
Point Mining for better key point extraction; integrating sentiment analysis
into KPA; identifying good key point candidates for review summaries; and
leveraging the massive amount of available reviews and their metadata. We show
empirically that these novel extensions of KPA substantially improve its
performance. We demonstrate that promising results can be achieved without any
domain-specific annotation, while human supervision can lead to further
improvement.
