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

DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization