document: Unified analysis of streaming news News clustering , categorization and analysis are key components of any news portal . They require algorithms capable of dealing with dynamic data to cluster , interpret and to temporally aggregate news articles . These three tasks are often solved separately . In this paper we present a unified framework to group incoming news articles into temporary but tightly-focused storylines , to identify prevalent topics and key entities within these stories , and to reveal the temporal structure of stories as they evolve . We achieve this by building a hybrid clustering and topic model . To deal with the available wealth of data we build an efficient parallel inference algorithm by sequential Monte Carlo estimation . Time and memory costs are nearly constant in the length of the history , and the approach scales to hundreds of thousands of documents . We demonstrate the efficiency and accuracy on the publicly available TDT dataset and data of a major internet news site .
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