This paper describes the IRIT lab participation to theVital Filtering task (also known asCumulative Citation Recommendation) of the TREC 2014 Knowledge Base Acceleration Track. This task aims at identifying vital documents containing timely new information that should help a human to update the profile of the target entity (e.g., Wikipedia page of the entity). In this work, we evaluate two factors that could detect vitality. The first one uses a Language Model to learn vitality from a sample of vital documents, and the second leverages the bursts of documents in the stream. Obtained results are presented and discussed.