Multilingual Clustering of Streaming News
This enables efficient media monitoring by aggregating multilingual news articles into stories, addressing a domain-specific need for scalable processing of massive news streams.
The paper tackles the problem of clustering streaming news across multiple languages into coherent stories in an online setting, achieving state-of-the-art results on German, English, and Spanish datasets with a method that is simple, computationally efficient, and scalable.
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.