survey

A Survey of Knowledge-enhanced Text Generation

Authors:

Meng JiangAuthors Info & Claims

ACM Computing Surveys, Volume 54, Issue 11s

Article No.: 227, Pages 1 - 38

https://doi.org/10.1145/3512467

Published: 10 November 2022 Publication History

Abstract

The goal of text-to-text generation is to make machines express like a human in many applications such as conversation, summarization, and translation. It is one of the most important yet challenging tasks in natural language processing (NLP). Various neural encoder-decoder models have been proposed to achieve the goal by learning to map input text to output text. However, the input text alone often provides limited knowledge to generate the desired output, so the performance of text generation is still far from satisfaction in many real-world scenarios. To address this issue, researchers have considered incorporating (i) internal knowledge embedded in the input text and (ii) external knowledge from outside sources such as knowledge base and knowledge graph into the text generation system. This research topic is known as knowledge-enhanced text generation. In this survey, we present a comprehensive review of the research on this topic over the past five years. The main content includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data. This survey can have broad audiences, researchers and practitioners, in academia and industry.

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Index Terms

A Survey of Knowledge-enhanced Text Generation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. General and reference
  1. Document types
    1. Surveys and overviews

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 11s

January 2022

785 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551650

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 10 November 2022

Online AM: 25 March 2022

Accepted: 10 January 2022

Revised: 26 October 2021

Received: 20 October 2020

Published in CSUR Volume 54, Issue 11s

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