  We propose DeepChannel, a robust, data-efficient, and interpretable neural
model for extractive document summarization. Given any document-summary pair,
we estimate a salience score, which is modeled using an attention-based deep
neural network, to represent the salience degree of the summary for yielding
the document. We devise a contrastive training strategy to learn the salience
estimation network, and then use the learned salience score as a guide and
iteratively extract the most salient sentences from the document as our
generated summary. In experiments, our model not only achieves state-of-the-art
ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the
out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1
F-1 score of 39.41 on CNN/Daily Mail test set with merely $1 / 100$ training
set, demonstrating a tremendous data efficiency.
