Question: What are the author's main contributions?
Evidence: Following developing news stories is imperative to making real-time decisions on important political and public safety matters. Given the abundance of media providers and languages, this endeavor is an extremely difficult task. As such, there is a strong demand for automatic clustering of news streams, so that they can be organized into stories or themes for further processing. Performing this task in an online and efficient manner is a challenging problem, not only for newswire, but also for scientific articles, online reviews, forum posts, blogs, and microblogs.
A key challenge in handling document streams is that the story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream. In this paper, we provide a treatment to the problem of online document clustering, i.e. the task of clustering a stream of documents into themes. For example, for news articles, we would want to cluster them into related news stories.
To this end, we introduce a system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. Our clustering approach is part of a larger media monitoring project to solve the problem of monitoring massive text and TV/Radio streams (speech-to-text). In particular, media monitors write intelligence reports about the most relevant events, and being able to search, visualize and explore news clusters assists in gathering more insight about a particular story. Since relevant events may be spawned from any part of the world (and from many multilingual sources), it becomes imperative to cluster news across different languages.
In terms of granularity, the type of story clusters we are interested in are the group of articles which, for example : (i) Narrate recent air-strikes in Eastern Ghouta (Syria); (ii) Describe the recent launch of Space X's Falcon Heavy rocket.
Our main contributions are as follows:  • We develop a system that aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. As discussed in the introduction, this is a highly relevant task for the use-case of media monitoring.  • We formulate the problem of online multilingual document clustering and the representation that such clustering takes by interlacing the problem  of monolingual clustering with crosslingual clustering. The representation of our clusters is interpretable, and similarly to topic models, consists of a set of keywords and weights associated with the relevant cluster. In our formulation, a monolingual cluster is a group of documents, and a crosslingual cluster is a group of monolingual clusters in different languages.  • We compare our approach to our own implementation of a state-of-the-art streaming method, and show much superior results for a dataset in English, Spanish and German.
Answer: • They develop a system that aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. • They formulate the problem of online multilingual document clustering and the representation that such clustering takes by interlacing the problem  of monolingual clustering with crosslingual clustering. • They compare their approach to their own implementation of a state-of-the-art streaming method, and show much superior results for a dataset in English, Spanish and German.