In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG). The primary objective is to generate text that is both linguistically natural and human-like, while also exerting control over the generation process. This paper offers a comprehensive and task-agnostic survey of the recent advancements in neural text generation. These advancements have been facilitated through a multitude of developments, which we categorize into four key areas: data construction, neural frameworks, training and inference strategies, and evaluation metrics. By examining these different aspects, we aim to provide a holistic overview of the progress made in the field. Furthermore, we explore the future directions for the advancement of neural text generation, which encompass the utilization of neural pipelines and the incorporation of background knowledge. These avenues present promising opportunities to further enhance the capabilities of NLG systems. Overall, this survey serves to consolidate the current state of the art in neural text generation and highlights potential avenues for future research and development in this dynamic field.

翻译：近年来，大量研究聚焦于神经模型在自然语言生成领域的应用。其核心目标是生成兼具语言自然性与人类风格特性的文本，同时实现对生成过程的精确控制。本文提供了一项全面且任务无关的综述，系统梳理了神经文本生成领域的最新进展。这些进展通过多种技术路线实现，我们将其归纳为四个关键维度：数据构建、神经架构、训练与推理策略，以及评估指标。通过系统审视这些不同维度，我们旨在呈现该领域取得进展的整体图景。此外，我们探讨了神经文本生成未来发展的方向，包括神经流水线的应用与背景知识的融合。这些方向为提升自然语言生成系统的能力提供了富有前景的机遇。总体而言，本综述致力于整合神经文本生成领域当前的技术发展水平，并揭示该动态领域未来研究与实践的潜在路径。