Language-based Construction of Explorable News Graphs for Journalists
Rémi Bois | Guillaume Gravier | Eric Jamet | Emmanuel Morin | Pascale Sébillot | Maxime Robert
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Faced with ever-growing news archives, media professionals are in need of advanced tools to explore the information surrounding specific events. This problem is most commonly answered by browsing news datasets, going from article to article and viewing unaltered original content. In this article, we introduce an efficient way to generate links between news items, allowing such browsing through an easily explorable graph, and enrich this graph by automatically typing links in order to inform the user on the nature of the relation between two news pieces. User evaluations are conducted on real world data with journalists in order to assess for the interest of both the graph representation and link typing in a press reviewing task, showing the system to be of significant help for their work.