  This work presents a thorough review concerning recent studies and text
generation advancements using Generative Adversarial Networks. The usage of
adversarial learning for text generation is promising as it provides
alternatives to generate the so-called "natural" language. Nevertheless,
adversarial text generation is not a simple task as its foremost architecture,
the Generative Adversarial Networks, were designed to cope with continuous
information (image) instead of discrete data (text). Thus, most works are based
on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement
Learning, and modified training objectives. All alternatives are reviewed in
this survey as they present the most recent approaches for generating text
using adversarial-based techniques. The selected works were taken from renowned
databases, such as Science Direct, IEEEXplore, Springer, Association for
Computing Machinery, and arXiv, whereas each selected work has been critically
analyzed and assessed to present its objective, methodology, and experimental
results.
