  Abstractive text summarization is the task of compressing and rewriting a
long document into a short summary while maintaining saliency, directed logical
entailment, and non-redundancy. In this work, we address these three important
aspects of a good summary via a reinforcement learning approach with two novel
reward functions: ROUGESal and Entail, on top of a coverage-based baseline. The
ROUGESal reward modifies the ROUGE metric by up-weighting the salient
phrases/words detected via a keyphrase classifier. The Entail reward gives high
(length-normalized) scores to logically-entailed summaries using an entailment
classifier. Further, we show superior performance improvement when these
rewards are combined with traditional metric (ROUGE) based rewards, via our
novel and effective multi-reward approach of optimizing multiple rewards
simultaneously in alternate mini-batches. Our method achieves the new
state-of-the-art results (including human evaluation) on the CNN/Daily Mail
dataset as well as strong improvements in a test-only transfer setup on
DUC-2002.
