Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

TLDR

An up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised is given, to highlight a number of recent research topics that have arisen partly as a result of growing synergies betweenNLG and other areas of artifical intelligence.

Abstract

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artifical intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of nlp, with an emphasis on different evaluation methods and the relationships between them.