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：计算叙事提取是人工智能的子领域,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制定该领域未来研究战略方面却相对没有多少学术工作。特别是,本篇文章侧重于从事件中心角度提取新闻报道。从新闻数据提取叙事在了解不断变化的信息景观方面有着多种应用。本调查展示了对事件新闻叙事提取领域研究的广泛研究。特别是,我们筛选了900多篇产生54条相关文章的文章。这些文章是按代表性模型、提取标准和评价方法合成和整理的。我们根据所审查的研究,确定了最近的趋势、公开的挑战和潜在的研究路线。