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 over 900 articles that 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.

翻译：叙事是我们理解世界的基础，为随时间演进的知识表示提供了天然结构。计算叙事提取是人工智能的一个子领域，其核心依赖于信息检索与自然语言处理技术。尽管计算叙事提取具有重要性，但现有学术研究在系统整合前人成果及规划该领域未来发展方面仍相对欠缺。本文特别聚焦于从事件中心视角提取新闻叙事。从新闻数据中提取叙事对于理解持续演变的信息格局具有多重应用价值。本综述对基于事件的新闻叙事提取领域的研究进行了系统梳理。具体而言，我们筛选了900余篇论文，最终纳入54篇相关文献。这些文献按其表征模型、提取标准及评估方法进行了分类整合。基于对现有研究的分析，我们识别出当前趋势、开放挑战及潜在研究方向。