Automatic generation of semantically well‐formed questions from a given text can contribute to various domains, including education, dialogues/interactive question answering systems, search engines, and more. It is well‐known as a challenging task, which involves the common obstacles of other natural language processing (NLP) activities. We start this advanced review with a brief overview of the most common automatic question generation (AQG) applications. Then we describe the main steps of a typical AQG pipeline, namely question construction, ranking, and evaluation. Finally, we discuss the open challenges of the AQG field that still need to be addressed by NLP researchers.

中文翻译：

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自动生成问题

从给定的文本中自动生成语义上正确的问题可以有助于各个领域，包括教育，对话/交互式问题回答系统，搜索引擎等等。这是一项艰巨的任务，众所周知，它涉及其他自然语言处理（NLP）活动的常见障碍。我们将从对最常见的自动问题生成（AQG）应用程序的简要概述开始此高级回顾。然后，我们描述了典型的AQG流程的主要步骤，即问题构建，排名和评估。最后，我们讨论了NLP研究人员仍需解决的AQG领域的开放挑战。