A novel semantic-enhanced generative adversarial network for abstractive text summarization

Recently, for the abstractive summarization task, most of proposed techniques have adopted the deep recurrent neural network (RNN)-based sequential auto-encoding architecture to effectively learn and generate meaningful summaries for different input documents. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. In addition, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also makes the achieved summaries unnatural and incoherent. In order to deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model which trains the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.