Generating Semantically Similar and Human-Readable Summaries With Generative Adversarial Networks

The application of neural networks in natural language processing, including abstractive text summarization, is increasingly attractive in recent years. However, teaching a neural network to generate a human-readable summary that reflects the core idea of the original source text (i.e., semantically similar) remains a challenging problem. In this paper, we explore using generative adversarial networks to solve this problem. The proposed model contains three components: a generator that encodes the long input text into a shorter representation; a discriminator to teach the generator to create human-readable summaries and another discriminator to restrict the output of the generator to reflect the core idea of the input text. The main training process can be carried out in an adversarial learning process. To solve the non-differentiable problem caused by the words sampling process, we use the policy gradient algorithm to optimize the generator. We evaluate the proposed model on the CNN/Daily Mail summarization task. The experimental results show that the model outperforms previous state-of-the-art models.