  Acquiring accurate summarization and sentiment from user reviews is an
essential component of modern e-commerce platforms. Review summarization aims
at generating a concise summary that describes the key opinions and sentiment
of a review, while sentiment classification aims to predict a sentiment label
indicating the sentiment attitude of a review. To effectively leverage the
shared sentiment information in both review summarization and sentiment
classification tasks, we propose a novel dual-view model that jointly improves
the performance of these two tasks. In our model, an encoder first learns a
context representation for the review, then a summary decoder generates a
review summary word by word. After that, a source-view sentiment classifier
uses the encoded context representation to predict a sentiment label for the
review, while a summary-view sentiment classifier uses the decoder hidden
states to predict a sentiment label for the generated summary. During training,
we introduce an inconsistency loss to penalize the disagreement between these
two classifiers. It helps the decoder to generate a summary to have a
consistent sentiment tendency with the review and also helps the two sentiment
classifiers learn from each other. Experiment results on four real-world
datasets from different domains demonstrate the effectiveness of our model.
