\section{Conclusion}\label{Conclusion}

Inspired by the reading cognition of human beings, we propose HER, a two-stage method, to mimic how people extract summaries. 
The whole learning process is formulated as a contextual bandit trained with policy gradient reinforcement learning.
In rough reading, two neural networks are taken to encode coarse-grained information. In careful reading, repeatedly reading are conducted to select fine-grained sentences as summary. %The whole process is formulated as a contextual bandit trained with policy gradient reinforcement learning.
Experiments on two real-world datasets demonstrate that our proposed model can significantly outperform the state-of-the-art extractive methods on summary quality, coverage and non-redundancy. 