A Reinforced Topic Aware Convolutional Sequence to Sequence Model for Abstractive Text Summarization
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence to sequence (ConvS2S) model and using self critical sequence training (SCST) for optimization. Through jointly attending to topics and word level alignment, our approach can improve coherence, diversity, and informativeness of generated summaries via a biased probability generation mechanism. On the other hand, reinforcement training, like SCST, directly optimizes the proposed model with respect to the non differentiable metric ROUGE, which also avoids the exposure bias during inference. We carry out the experimental evaluation with state of the art methods over the Gigaword, DUC 2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.
IJCAI