Text Generation aims to produce plausible and readable text in human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this article, we provide a survey on the utilization of PLMs in text generation. We begin with introducing two key aspects of applying PLMs to text generation: (1) how to design an effective PLM to serve as the generation model; and (2) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties. Then, we show the major challenges that have arisen in these aspects, as well as possible solutions for them. We also include a summary of various useful resources and typical text generation applications based on PLMs. Finally, we highlight the future research directions which will further improve these PLMs for text generation. This comprehensive survey is intended to help researchers interested in text generation problems to learn the core concepts, the main techniques and the latest developments in this area based on PLMs.

文本生成旨在从输入数据生成可信且可读的人类语言文本。深度学习的复兴极大地推动了这一领域的发展，特别是在基于预训练语言模型（PLM）的神经生成模型的帮助下。基于 PLM 的文本生成在学术界和工业界都被视为一种有前途的方法。在本文中，我们对 PLM 在文本生成中的应用进行了调查。我们首先介绍将PLM应用于文本生成的两个关键方面：（1）如何设计有效的PLM作为生成模型； (2)如何在给定参考文本的情况下有效优化PLM并确保生成的文本满足特殊文本属性。然后，我们展示了这些方面出现的主要挑战以及可能的解决方案。我们还总结了各种有用的资源和基于 PLM 的典型文本生成应用程序。最后，我们强调了未来的研究方向，将进一步改进这些用于文本生成的 PLM。这项综合调查旨在帮助对文本生成问题感兴趣的研究人员了解该领域基于 PLM 的核心概念、主要技术和最新进展。