A Survey on Event-based News Narrative Extraction
arXiv:2302.08351 · doi:10.1145/3584741
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.
arXiv comment37 pages, 3 figures, to be published in the journal ACM CSUR
References in corpus (6)
- LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
- The Future of Misinformation Detection: New Perspectives and Trends
- Event Prediction in the Big Data Era: A Systematic Survey
- Analyzing Evolving Stories in News Articles
- Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives
- Time-dependent Hierarchical Dirichlet Model for Timeline Generation