  With the increasing number of merchandise on e-commerce platforms, users tend
to refer to reviews of other shoppers to decide which product they should buy.
However, with so many reviews of a product, users often have to spend lots of
time browsing through reviews talking about product attributes they do not care
about. We want to establish a system that can automatically summarize and
answer user's product specific questions.
  In this study, we propose a novel seq2seq based text generation model to
generate answers to user's question based on reviews posted by previous users.
Given a user question and/or target sentiment polarity, we extract aspects of
interest and generate an answer that summarizes previous relevant user reviews.
Specifically, our model performs attention between input reviews and target
aspects during encoding and is conditioned on both review rating and input
context during decoding. We also incorporate a pre-trained auxiliary rating
classifier to improve model performance and accelerate convergence during
training. Experiments using real-world e-commerce dataset show that our model
achieves improvement in performance compared to previously introduced models.
