Towards information-rich, logical text generation with
  knowledge-enhanced neural models
  Text generation system has made massive promising progress contributed by
deep learning techniques and has been widely applied in our life. However,
existing end-to-end neural models suffer from the problem of tending to
generate uninformative and generic text because they cannot ground input
context with background knowledge. In order to solve this problem, many
researchers begin to consider combining external knowledge in text generation
systems, namely knowledge-enhanced text generation. The challenges of knowledge
enhanced text generation including how to select the appropriate knowledge from
large-scale knowledge bases, how to read and understand extracted knowledge,
and how to integrate knowledge into generation process. This survey gives a
comprehensive review of knowledge-enhanced text generation systems, summarizes
research progress to solving these challenges and proposes some open issues and
research directions.
