A Survey on Event-based News Narrative Extraction

TL;DR

This survey reviews over 50 studies on event-based news narrative extraction, highlighting recent trends, challenges, and future research directions in computational narrative understanding from news data.

Contribution

It provides a comprehensive synthesis of existing research, categorizing methods and evaluation approaches, and identifies key open challenges and future research opportunities.

Findings

Identified recent trends in event-based news narrative extraction

Highlighted open challenges in the field

Suggested potential future research directions

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 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.