Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened more than 900 articles, which yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.

叙述是我们理解世界的基础，随着时间的推移，它为我们提供了知识表示的自然结构。计算叙述提取是人工智能的一个子领域，它大量使用信息检索和自然语言处理技术。尽管计算叙事提取很重要，但在综合先前的研究和规划该领域未来的研究方面的学术工作相对较少。特别是，本文重点关注从以事件为中心的角度提取新闻叙述。从新闻数据中提取叙述在理解不断变化的信息格局方面具有多种应用。这项调查展示了基于事件的新闻叙事提取领域的广泛研究。尤其，我们筛选了 900 多篇文章，产生了 54 篇相关文章。这些文章通过表示模型、提取标准和评估方法进行综合和组织。根据审查的研究，我们确定了最近的趋势、开放的挑战和潜在的研究方向。