The goal of text generation is to make machines express in human language. It
is one of the most important yet challenging tasks in natural language
processing (NLP). Since 2014, various neural encoder-decoder models pioneered
by Seq2Seq have been proposed to achieve the goal by learning to map input text
to output text. However, the input text alone often provides limited knowledge
to generate the desired output, so the performance of text generation is still
far from satisfaction in many real-world scenarios. To address this issue,
researchers have considered incorporating various forms of knowledge beyond the
input text into the generation models. This research direction is known as
knowledge-enhanced text generation. In this survey, we present a comprehensive
review of the research on knowledge enhanced text generation over the past five
years. The main content includes two parts: (i) general methods and
architectures for integrating knowledge into text generation; (ii) specific
techniques and applications according to different forms of knowledge data.
This survey can have broad audiences, researchers and practitioners, in
academia and industry.

本文综述了过去五年间关于知识增强文本生成的研究，主要内容包括将各种形式的知识融合到文本生成中的通用方法与架构，以及基于不同形式的知识数据的具体技术和应用。