In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The survey begins with an overview of NQG's background, encompassing the task's problem formulation, prevalent benchmark datasets, established evaluation metrics, and notable applications. It then methodically classifies NQG approaches into three predominant categories: structured NQG, which utilizes organized data sources, unstructured NQG, focusing on more loosely structured inputs like texts or visual content, and hybrid NQG, drawing on diverse input modalities. This classification is followed by an in-depth analysis of the distinct neural network models tailored for each category, discussing their inherent strengths and potential limitations. The survey culminates with a forward-looking perspective on the trajectory of NQG, identifying emergent research trends and prospective developmental paths. Accompanying this survey is a curated collection of related research papers, datasets and codes, systematically organized on Github, providing an extensive reference for those delving into NQG.

翻译：本综述系统审视了神经问题生成（Neural Question Generation, NQG）领域的最新进展。该领域利用神经网络技术，从知识库、文本及图像等多源输入中自动生成关联性问题。我们首先概述了NQG的研究背景，涵盖任务形式化定义、主流基准数据集、公认评估指标及典型应用场景。继而将现有方法划分为三大主流范式：结构化NQG（利用有序数据源）、非结构化NQG（聚焦文本或视觉内容等半结构化输入）以及混合NQG（融合多模态输入）。针对每类范式，我们深入剖析了其适配的神经架构特性，探讨了相应模型的固有优势与潜在局限。最后，本文以前瞻性视角审视了NQG的发展轨迹，识别出新兴研究趋势与潜在发展方向。与之配套的Github资源库系统整理了相关研究论文、数据集及代码实现，为NQG领域研究者提供了全面参考。