Affiliation:
1. Virginia Tech and Universidad Católica del Norte
2. University of Washington
3. Virginia Tech
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.
Funder
NSF
Virginia Tech ICTAS Junior Faculty
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference135 articles.
1. The Cambridge Introduction to Narrative
2. Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, and Patrick Martineau. 2015. An exact graph edit distance algorithm for solving pattern recognition problems. In Proceedings of the 2015 4th International Conference on Pattern Recognition Applications and Methods.1–9.
3. Automatic story generation: A survey of approaches;Alhussain Arwa I.;ACM Computing Surveys,2021
4. James Allan. 2012. Topic Detection and Tracking: Event-Based Information Organization. Vol. 12. Springer Science & Business Media, New York, NY.
5. Giang Binh Tran, Mohammad Alrifai, and Dat Quoc Nguyen. 2013. Predicting Relevant News Events for Timeline Summaries. In Proceedings of the 22nd International Conference on World Wide Web (WWW’13). Association for Computing Machinery, 91–92.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献