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，为随时间演变的知识表征提供了自然结构。计算叙事抽取是人工智能的一个子领域，大量运用信息检索和自然语言处理技术。尽管计算叙事抽取具有重要意义，但关于综合先前研究并规划该领域未来研究方向的相关学术工作相对较少。本文特别关注从事件中心视角抽取新闻叙事。从新闻数据中抽取叙事对于理解不断演进的信息格局具有多重应用价值。本综述对该领域基于事件的新闻叙事抽取研究进行了广泛调研，具体筛选了900余篇论文并最终选取54篇相关文献。这些文献按照表征模型、抽取准则和评估方法进行综合归纳与分类。基于现有研究，我们识别出近期趋势、开放挑战及潜在研究方向。