A Topic broadly represents the contents in a text and is also the subject the text refers to that can be used to enhance natural text generation.

LDA( Latent Dirichlet allocation) is a classical topic modelling tool for inferring low dimensional representation that captures the underlying semantics of words and documents. In LDA each topic is defined as a distribution over words and each document as a mixture distribution over topics. LDA gives a set of distributions of topics for each document and also a set of distributions of words for each topic

Topic enhanced NLG are categorized into

• Leverage Topic Words from Generative Topic Models
• Jointly Optimize Generation Model and CNN Topic Model.
• Enhance NLG by Neural Topic Models with Variational Inference