Exploring Entity-centric Networks in Entangled News Streams.

The increasing number of news outlets and the frequency of the news cycle have made it all but impossible to obtain the full picture from online news. Consolidating news from different sources has thus become a necessity in online news processing. Despite the amount of research that has been devoted to different aspects of new event detection and tracking in news streams, solid solutions for such entangled streams of full news articles are still lacking. Many existing works focus on streams of microblogs since the analysis of news articles raises the additional problem of summarizing or extracting the relevant sections of articles. For the consolidation of identified news snippets, schemes along numerous different dimensions have been proposed, including publication time, temporal expressions, geo-spatial references, named entities, and topics. The granularity of aggregated news snippets then includes such diverse aspects as events, incidents, threads, or topics for various subdivisions of news articles. To support this variety of granularity levels, we propose a comprehensive network model for the representation of multiple entangled streams of news documents. Unlike previous methods, the model is geared towards entity-centric explorations and enables the consolidation of news along all dimensions, including the context of entity mentions. Since the model also serves as a reverse index, it supports explorations along the dimensions of sentences or documents for an encompassing view on news events. We evaluate the performance of our model on a large collection of entangled news streams from major news outlets of English speaking countries and a ground truth that we generate from event summaries in the Wikipedia Current Events portal.