  Clustering news across languages enables efficient media monitoring by
aggregating articles from multilingual sources into coherent stories. Doing so
in an online setting allows scalable processing of massive news streams. To
this end, we describe a novel method for clustering an incoming stream of
multilingual documents into monolingual and crosslingual story clusters. Unlike
typical clustering approaches that consider a small and known number of labels,
we tackle the problem of discovering an ever growing number of cluster labels
in an online fashion, using real news datasets in multiple languages. Our
method is simple to implement, computationally efficient and produces
state-of-the-art results on datasets in German, English and Spanish.
