  In this paper, we study abstractive review summarization.Observing that
review summaries often consist of aspect words, opinion words and context
words, we propose a two-stage reinforcement learning approach, which first
predicts the output word type from the three types, and then leverages the
predicted word type to generate the final word distribution.Experimental
results on two Amazon product review datasets demonstrate that our method can
consistently outperform several strong baseline approaches based on ROUGE
scores.

Computation and Language;Information Retrieval;Machine Learning