Towards the automatic analysis of the structure of news stories

News stories are distinct from other types of narratives in that they typically follow a complex and non-chronological time structure. This poses challenges to the narrative analysis of news, specifically with respect to the construction of event sequences. In this paper, we propose to segment news story text according to news schema categories, which allow for identifying sentences describing a news story’s main action and other actions that happened beforehand or subsequently. To automate this task, we made observations on the linguistic devices that are used by news writers, based on a manually annotated corpus of news articles that we have constructed. Heuristics capturing these linguistic devices were then developed, underpinned by natural languageprocessing tools as well as carefully curated look-up lists of cues. While encouraging preliminary results were obtained, the work can be further expanded by observing and capturing more linguistic devices, which can be facilitated by further annotation of news stories based on news schema categories.