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A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception

Jones, Keenan, Altuncu, Enes, Franqueira, Virginia N. L., Wang, Yichao, Li, Shujun (2022) A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception. arXiv, . (Submitted) (doi:10.48550/arXiv.2208.05757) (KAR id:97944)

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In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.

Item Type: Article
DOI/Identification number: 10.48550/arXiv.2208.05757
Uncontrolled keywords: Natural Language Generation, NLG, Digital Deception, Survey, Taxonomy
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
University-wide institutes > Institute of Cyber Security for Society
Funders: University of Kent (
Depositing User: Virginia Franqueira
Date Deposited: 13 Nov 2022 09:44 UTC
Last Modified: 14 Nov 2022 15:31 UTC
Resource URI: (The current URI for this page, for reference purposes)
Jones, Keenan:
Franqueira, Virginia N. L.:
Wang, Yichao:
Li, Shujun:
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