We propose a unified model combining the strength of extractive and abstractive summarization.On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable.On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph.In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated.Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions.By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-theart ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.