Online News Tracking for Ad-Hoc Information Needs

Following online news about a specific event can be a difficult task as new information is often scattered across web pages. In such cases, 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 propose a three-step approach to online news tracking for ad-hoc information needs. First, we continuously cluster the titles of all incoming news articles. Then, we select the clusters that best fit a user's ad-hoc information need and identify salient sentences. Finally, we select sentences for the summary based on novelty and relevance to the information seen, without requiring an a-priori model of events of interest. We evaluate this approach using the 2013 TREC Temporal Summarization test set and show that compared to existing systems our approach retrieves news facts with significantly higher F-measure and Latency-Discounted Expected Gain.