Taking advantage of Big Data while retaining a user-centered point of view is quite difficult. Managing data volume, variety and velocity to extract the relevant information is still challenging. The information extraction needs customization to adapt both content and presentation to fit users' current profile. Regarding the content, data volume can be reduced and personalized by using user preferences. Regarding presentation, answers should be adapted to be displayed on the user devices. This talk focuses on improving stream data monitoring by proposing the construction of ad-hoc summaries. We will present a comprehensive solution which relies on a personalized and continuous refinement of data in order to generate texts that provide a tailored synthesis of relevant data. Short texts in natural language will summarize the result of continuous complex data monitoring. The presented solution adopts contextual preferences to better fit users current priorities. Text summaries can be shared on social networks and delivered to personal devices in various contexts (e.g. listen to summaries while driving).

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