A Survey of Knowledge-Enhanced Text Generation. (arXiv:2010.04389v3 [cs.CL] UPDATED)

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 …

arxiv survey text text generation