survey

A Survey of Natural Language Generation

Authors:

Min YangAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 8

Article No.: 173, Pages 1 - 38

https://doi.org/10.1145/3554727

Published: 23 December 2022 Publication History

Abstract

This article offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text, and computational creativity.

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Index Terms

A Survey of Natural Language Generation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 8

August 2023

789 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567473

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 23 December 2022

Online AM: 03 August 2022

Accepted: 25 July 2022

Revised: 02 July 2022

Received: 10 August 2021

Published in CSUR Volume 55, Issue 8

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Lin YRuan TLiu JWang H(2024)A Survey on Neural Data-to-Text GenerationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330438536:4(1431-1449)Online publication date: Apr-2024
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