  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.
