Sequence Generative Adversarial Network for Long Text Summarization

Abstract

In this paper, we propose a new adversarial training framework for text summarization task. Although sequence-to-sequence models have achieved state-of-the-art performance in abstractive summarization, the training strategy (MLE) suffers from exposure bias in the inference stage. This discrepancy between training and inference makes generated summaries less coherent and accuracy, which is more prominent in summarizing long articles. To address this issue, we model abstractive summarization using Generative Adversarial Network (GAN), aiming to minimize the gap between generated summaries and the ground-truth ones. This framework consists of two models: a generator that generates summaries, a discriminator that evaluates generated summaries. Reinforcement learning (RL) strategy is used to guarantee the co-training of generator and discriminator. Besides, motivated by the nature of summarization task, we design a novel Triple-RNNs discriminator, and extend the off-the-shelf generator by appending encoder and decoder with attention mechanism. Experimental results showed that our model significantly outperforms the state-of-the-art models, especially on long text corpus.