Generating overview timelines for major events in an RSS corpus

Really simple syndication (RSS) is becoming a ubiquitous technology for notifying users of new content in frequently updated web sites, such as blogs and news portals. This paper describes a feature-based, local clustering approach for generating overview timelines for major events, such as the tsunami tragedy, from a general-purpose corpus of RSS feeds. In order to identify significant events, we automatically (1) selected a set of significant terms for each day; (2) built a set of (term–co-term) pairs and (3) clustered the pairs in an attempt to group contextually related terms. The clusters were assessed by 10 people, finding that the average percentage apparently representing significant events was 68.6%. Using these clusters, we generated overview timelines for three major events: the tsunami tragedy, the US election and bird flu. The results indicate that our approach is effective in identifying predominantly genuine events, but can only produce partial timelines.