Ranking Multidocument Event Descriptions for Building Thematic Timelines

This paper tackles the problem of timeline generation from traditional news sources. Our system builds thematic timelines for a general-domain topic defined by a user query. The system selects and ranks events relevant to the input query. Each event is represented by a one-sentence description in the output timeline. We present an inter-cluster ranking algorithm that takes events from multiple clusters as input and that selects the most salient and relevant events. A cluster, in our work, contains all the events happening in a specific date. Our algorithm utilizes the temporal information derived from a large collection of extensively temporal analyzed texts. Such temporal information is combined with textual contents into an event scoring model in order to rank events based on their salience and query-relevance.