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News events such as protests, accidents or natural disasters represent a unique information access problem where traditional approaches fail. For example, immediately after an event, the corpus may be sparsely populated with relevant content. Even when, after a few hours, relevant content becomes available, it is often inaccurate or highly redundant. At the same time, crisis events demonstrate a scenario where users urgently need information, especially if they are directly affected by the event. The goal of this track is to develop systems for efficiently monitoring the information associated with an event over time. Specifically, we are interested in developing systems which can broadcast short, relevant, and reliable sentence-length updates about a developing event. The track has the following four main aims To develop algorithms which detect sub-events with low latency. To model information reliability in the presence of a dynamic corpus. To understand and address the sensitivity of text summarization algorithms in an online, sequential setting, and to understand and address the sensitivity of information extraction algorithms in dynamic settings.

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