Abstract
In this paper, we propose an improved hybrid approach for text generation that combines the strengths of generative adversarial networks (GANs) and recurrent neural networks (RNNs). The proposed model, named TextGen-GAN, uses a GAN to generate high-level semantic representations of text, which are then passed through an RNN decoder to produce coherent and diverse text. The suggested method produces superior results in terms of both quality and diversity when tested on a number of benchmark datasets. The obtained results show that the combination of GAN and RNN outperforms GAN alone, RNN only, baseline, and state-of-the-art models, demonstrating the effectiveness of the proposed hybrid approach.