Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in their narrative sensemaking process. Narrative maps allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model the connection between storylines. We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking. We perform two experiments with a data set of news articles. The insights extracted from our studies can be used to design narrative maps, extraction algorithms, and visual analytics tools to support the narrative sensemaking process. The contributions of this paper are three-fold: (1) an analysis of how analysts construct narrative maps; (2) a user evaluation of specific narrative map features; and (3) design guidelines for narrative maps. Our findings suggest ways for designing narrative maps and extraction algorithms, as well as providing insights toward useful interactions. We discuss these insights and design guidelines and reflect on the potential challenges involved. As key highlights, we find that narrative maps should avoid redundant connections that can be inferred by using the transitive property of event connections, reducing the overall complexity of the map. Moreover, narrative maps should use multiple types of cognitive connections between events such as topical and causal connections, as this emulates the strategies that analysts use in the narrative sensemaking process.
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This content will become publicly available on December 31, 2024
A Survey on Event-Based News Narrative Extraction
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.
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- Award ID(s):
- 2128642
- NSF-PAR ID:
- 10439263
- Date Published:
- Journal Name:
- ACM Computing Surveys
- Volume:
- 55
- Issue:
- 14s
- ISSN:
- 0360-0300
- Page Range / eLocation ID:
- 1 to 39
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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