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As an attempt to combine extractive and abstractive summarization, SentenceRewriting models adopt the strategy of extracting salient sentences from adocument first and then paraphrasing the selected ones to generate a summary.However, the existing models in this framework mostly rely on sentence-levelrewards or suboptimal labels, causing a mismatch between a training objectiveand evaluation metric. In this paper, we present a novel training signal thatdirectly maximizes summary-level ROUGE scores through reinforcement learning.In addition, we incorporate BERT into our model, making good use of its abilityon natural language understanding. In extensive experiments, we show that acombination of our proposed model and training procedure obtains newstate-of-the-art performance on both CNN/Daily Mail and New York Timesdatasets. We also demonstrate that it generalizes better on DUC-2002 test set.

Summary Level Training of Sentence Rewriting for Abstractive Summarization