A Survey on Event-Based News Narrative Extraction

Norambuena, Brian Felipe Keith; Mitra, Tanushree; North, Chris

Abstract

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.

Más información

Título según WOS: A Survey on Event-Based News Narrative Extraction
Título de la Revista: ACM COMPUTING SURVEYS
Volumen: 55
Número: 14S
Editorial: ASSOC COMPUTING MACHINERY
Fecha de publicación: 2023
DOI:

10.1145/3584741

Notas: ISI