Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

翻译：事件提取(EE)是迅速从大量文字数据中获取事件信息的关键研究任务。随着深层学习的迅速发展,基于深层学习技术的EE已经成为一个研究热点。文献中提出了许多方法、数据集和评价指标,提高了进行全面和更新调查的必要性。本文章通过审查最新方法填补了研究差距,特别是基于深层学习模型的通用EEE方法。我们根据任务定义对当前一般领域EEE研究进行了新的文献分类。随后,我们总结了EE方法的范式和模式,然后详细讨论其中的每一种。作为重要方面,我们总结了支持预测和评价指标测试的基准。本调查还提供了不同方法的全面比较。最后,我们总结了研究领域未来的研究方向。