The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating DTG from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for DTG research that not only focus on the design of linguistically capable systems but also systems that exhibit fairness and accountability.

翻译：过去十年间，自然语言处理（NLP）领域的神经革命同样推动了数据到文本生成（DTG）的重大创新。本综述以结构化方式审视神经DTG范式，系统梳理了相关方法、基准数据集与评估协议。通过界定DTG与自然语言生成（NLG）领域中其他方向的边界，本文整合了最新文献综述，并突出展示了源自NLG体系内外部的技术采纳阶段。基于这一全局视角，我们揭示了DTG研究有前景的发展方向——不仅聚焦于具备语言能力的系统设计，更应构建体现公平性与可问责性的系统。