Nowadays journalistic content is distributed in multiple formats, mostly through the web and specific internet based applications running on smartphones and tablets. Text is a very important format, but readers (or more accurately users or information consumers) heavily rely on images, videos, slideshows, charts and infographics. Textual content is still the main representation for information. Any journalistic subject (e.g. Trump and Russia) is described in one or more texts produced by journalists and possibly commented by readers. Many of those subjects are followed during days, weeks or months. To grasp a possibly vast and somewhat complex set of interconnected news articles, readers would greatly benefit from tools that summarize those articles by showing main actors, their interplay and their trajectories in time and space, their motivations, main events, causal relations of events and outcomes. In other words, tools that extract narrative elements and re-represent them in formats that convey the essential story but that are more efficiently consumed by the users.\n\nThe aim of this project is very simple to state. We want to be able to extract narratives/stories from news articles or collections of related news articles (unstructured data) about the same (or related) subject, represent those narratives in intermediate data structures (structured data) and make this available to subsequent media production processes (semi-automatic generation of slide shows, infographics and other visualizations, video sequences, games, etc.). Although research on subsequent generation processes is not within the scope of this proposal, we will feature illustrative demos, and will in parallel and subsequently invest in those lines of research with groups specialized in each type of content. This is a very rich line of research that poses many challenging problems in information extraction and automatic production of media content.