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 F1​ score of up to 0.7. We conclude by
suggesting several promising directions for future work.Comment: Accpted to the First Joint Workshop on Narrative Understanding,
Storylines, and Events, ACL 202