CompRes: A Dataset for Narrative Structure in News
This work addresses the need for narrative analysis in news media, which is important for understanding social impact and public opinion, but it is incremental as it adapts existing theories to a new domain.
The paper tackled the problem of automatically detecting narrative structures in news articles by introducing CompRes, the first dataset for this purpose, and achieved an F1 score of up to 0.7 with supervised models.
This paper addresses the task of automatically detecting narrative structures in raw texts. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to news articles, motivated by their growing social impact as well as their role in creating and shaping public opinion. We introduce CompRes -- the first dataset for narrative structure in news media. We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for news media, by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success); then, we used that scheme to annotate a set of 29 English news articles (containing 1,099 sentences) collected from news and partisan websites. We use the annotated dataset to train several supervised models to identify the different narrative elements, achieving an $F_1$ score of up to 0.7. We conclude by suggesting several promising directions for future work.