Deep Differential Amplifier for Extractive Summarization.

For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when optimizing the classification. The imbalanced sentence classification in extractive summarization is inherent, which can't be addressed by data sampling or data augmentation algorithms easily. In order to address this problem, we innovatively consider the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework to enhance the features of summary sentences. Specifically, we calculate and amplify the semantic difference between each sentence and other sentences, and apply the residual unit to deepen the differential amplifier architecture. Furthermore, the corresponding objective loss of the minority class is boosted by a weighted cross-entropy. In this way, our model pays more attention to the pivotal information of one sentence, that is different from previous approaches which model all informative context in the source document. Experimental results on two benchmark datasets show that our summarizer performs competitively against state-of-the-art methods. Our source code will be available on Github.