A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization

A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization

Li Wang, Junlin Yao, Yunzhe Tao, Li Zhong, Wei Liu, Qiang Du

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4453-4460. https://doi.org/10.24963/ijcai.2018/619

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.
Keywords:
Machine Learning: Neural Networks
Natural Language Processing: Natural Language Summarization
Machine Learning: Deep Learning
Machine Learning Applications: Applications of Reinforcement Learning