First Story Detection (FSD) systems aim to identify those news articles that discuss an event that was not reported before. Recent work on FSD has focussed almost exclusively on efficiently detecting documents that are dissimilar from their nearest neighbor. We propose a novel FSD approach that is more effective, by adapting a recently proposed method for news summarization based on 3-nearest neighbor clustering. We show that this approach is more effective than a baseline that uses dissimilarity of an individual document from its nearest neighbor.

Following news about a specific event can be a difficult task as new information is often scattered across web pages. An up-to-date summary of the event would help to inform users and allow them to navigate to articles that are likely to contain relevant and novel details. We demonstrate an approach that is feasible for online tracking of news that is relevant to a user's ad-hoc query.