A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
This work addresses the problem of generating coherent and informative summaries for natural language processing applications, representing an incremental improvement over existing methods.
The paper tackles abstractive text summarization by integrating topic information into a convolutional sequence-to-sequence model and using reinforcement learning for optimization, achieving superior results on datasets like Gigaword, DUC-2004, and LCSTS.
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.