Discovering and Visualising Stories in News
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014)
Abstract
Daily news streams often revolve around topics that span over a longer period of time such as the global financial crisis or the healthcare debate in the US. The length and depth of these stories can be such that they become difficult to track for information specialists who need to reconstruct exactly what happened for policy makers and companies. We present a framework to model stories from news: we describe the characteristics that make up interesting stories, how these translate to filters on our data and we present a first use case in which we detail the steps to visualising story lines extracted from news articles about the global automotive industry.