This paper presents the participation of the IRIT laboratory (University of Toulouse) to the Real Time Summarization track of TREC 2016. This track consists in a real-time filtering the tweet stream and identifying both relevant and novel tweets to be pushed to user in real-time. Our team proposes three different approaches: (1) The first approach consist of a filtering model that combines several summarization constraints (2) The second approach for the scenario A is composed of three filters adjusted sequentially in which we use word similarity based function to evaluate the relevance of an incoming tweet. The generation of a batch of up to 100 ranked tweets is formulate as an optimization problem. (3) The third approach consist of a step by step stream selection method focusing on rapidity, and taking into account tweet similarity as well as several features including content, entities and user-related aspects. We describe in this paper the three proposed approaches and we discuss official obtained results for each of them.