Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Year, pages: 2017, 1 - 32

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.

Perera, R., Nand, P. 2017. Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature. In Computing and Informatics, vol. 36, no.1, pp. 1-32. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_1_1

Perera, R., Nand, P. (2017). Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature. Computing and Informatics, 36(1), 1-32. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_1_1