  A massive amount of reviews are generated daily from various platforms. It is
impossible for people to read through tons of reviews and to obtain useful
information. Automatic summarizing customer reviews thus is important for
identifying and extracting the essential information to help users to obtain
the gist of the data. However, as customer reviews are typically short,
informal, and multifaceted, it is extremely challenging to generate topic-wise
summarization.While there are several studies aims to solve this issue, they
are heuristic methods that are developed only utilizing customer reviews.
Unlike existing method, we propose an effective new summarization method by
analyzing both reviews and summaries.To do that, we first segment reviews and
summaries into individual sentiments. As the sentiments are typically short, we
combine sentiments talking about the same aspect into a single document and
apply topic modeling method to identify hidden topics among customer reviews
and summaries. Sentiment analysis is employed to distinguish positive and
negative opinions among each detected topic. A classifier is also introduced to
distinguish the writing pattern of summaries and that of customer reviews.
Finally, sentiments are selected to generate the summarization based on their
topic relevance, sentiment analysis score and the writing pattern. To test our
method, a new dataset comprising product reviews and summaries about 1028
products are collected from Amazon and CNET. Experimental results show the
effectiveness of our method compared with other methods.
