Abstract

Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on non-textual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology.

Keywords

MSC

68T50, 03B65

Cite this article

@article{cai-2838,
  author  = {Rivindu Perera and Parma Nand},
  title   = {Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature},
  journal = {Computing and Informatics},
  volume  = {36},
  number  = {1},
  pages   = {1--32},
  year    = {2017},
  issn    = {1335-9150},
  doi     = {10.4149/cai_2017_1_1}
}
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