Abstract
Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate text that meets grammatical and semantic requirements. In this paper, we sort and systematically summarize the main research progress in text generation and review recent text generation papers, focusing on presenting a detailed understanding of the technical models. In addition, several typical text generation application systems are presented. Finally, we address some challenges and future directions in AI text generation. We conclude that improving the quality, quantity, interactivity, and adaptability of generated text can help fundamentally advance AI text generation development.
摘要
文本生成是人工智能和自然语言处理的重要研究领域,为人工智能生成内容的快速发展提供了关键技术支撑。该任务基于自然语言处理、机器学习和深度学习等技术,通过训练模型学习语言规则,自动生成符合语法和语义要求的文本。本文对文本生成的主要研究进展进行梳理和系统性总结,对近几年文本生成相关文献进行综合调研,并详细介绍相关技术模型。此外,针对典型文本生成应用系统进行介绍。最后,对人工智能文本生成的挑战和未来研究方向进行分析和展望。得出以下结论,提高生成文本的质量、数量、交互性和适应性有助于从根本上推动人工智能文本生成的发展。
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Bing LI designed the research and drafted the paper. Peng YANG oversaw and led the planning and execution of the study. Yuankang SUN, Zhongjian HU, and Meng YI collected the information and revised the paper.
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Project supported by the National Natural Science Foundation of China (No. 62272100), the Consulting Project of Chinese Academy of Engineering (No. 2023-XY-09), the Major Project of the National Social Science Fund of China (No. 21ZD11), and the Fundamental Research Funds for the Central Universities, China
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Li, B., Yang, P., Sun, Y. et al. Advances and challenges in artificial intelligence text generation. Front Inform Technol Electron Eng 25, 64–83 (2024). https://doi.org/10.1631/FITEE.2300410
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DOI: https://doi.org/10.1631/FITEE.2300410