  Sentiment analysis provides a useful overview of customer review contents.
Many review websites allow a user to enter a summary in addition to a full
review. Intuitively, summary information may give additional benefit for review
sentiment analysis. In this paper, we conduct a study to exploit methods for
better use of summary information. We start by finding out that the sentimental
signal distribution of a review and that of its corresponding summary are in
fact complementary to each other. We thus explore various architectures to
better guide the interactions between the two and propose a
hierarchically-refined review-centric attention model. Empirical results show
that our review-centric model can make better use of user-written summaries for
review sentiment analysis, and is also more effective compared to existing
methods when the user summary is replaced with summary generated by an
automatic summarization system.
