A Survey on Event-based News Narrative Extraction
This survey addresses the need for a comprehensive overview in the subfield of AI focused on extracting news narratives from events, which is incremental as it synthesizes existing research rather than proposing new methods.
The authors tackled the lack of synthesis in computational narrative extraction by conducting a survey on event-based news narrative extraction, screening over 900 articles to analyze 54 relevant studies and organizing them by representation models, extraction criteria, and evaluation approaches.
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.