  Sentence scoring and sentence selection are two main steps in extractive
document summarization systems. However, previous works treat them as two
separated subtasks. In this paper, we present a novel end-to-end neural network
framework for extractive document summarization by jointly learning to score
and select sentences. It first reads the document sentences with a hierarchical
encoder to obtain the representation of sentences. Then it builds the output
summary by extracting sentences one by one. Different from previous methods,
our approach integrates the selection strategy into the scoring model, which
directly predicts the relative importance given previously selected sentences.
Experiments on the CNN/Daily Mail dataset show that the proposed framework
significantly outperforms the state-of-the-art extractive summarization models.
