  We study the problem of generating abstractive summaries for opinionated
text. We propose an attention-based neural network model that is able to absorb
information from multiple text units to construct informative, concise, and
fluent summaries. An importance-based sampling method is designed to allow the
encoder to integrate information from an important subset of input. Automatic
evaluation indicates that our system outperforms state-of-the-art abstractive
and extractive summarization systems on two newly collected datasets of movie
reviews and arguments. Our system summaries are also rated as more informative
and grammatical in human evaluation.
