Multi-Reward Reinforced Summarization with Saliency and Entailment

NAACL 2018  ·  Ramakanth Pasunuru, Mohit Bansal ·

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. read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Abstractive Text Summarization CNN / Daily Mail ROUGESal+Ent RL ROUGE-1 40.43 # 28
ROUGE-2 18.00 # 26
ROUGE-L 37.10 # 28


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