Internet users are turning more frequently to online news as a
\nreplacement for traditional media sources such as newspapers or
\ntelevision shows. Still, discovering news events online and follow-
\ning them as they develop can be a difficult task. In previous work,
\nwe presented a novel approach to extract sentences from an online
\nstream of news articles that summarizes the most important news
\nfacts for a given ad-hoc information need, which compared to ex-
\nisting systems obtained relatively high-precision results and a com-
\nparable recall [9]. In this track, we experiment with this approach
\nto improve the recall of retrieved results
