Linguistic summarization is a data mining and knowledge discovery approach to extract patterns and sum up large volume of data into simple sentences. There is a large research in generating linguistic summaries which can be used to better understand and communicate about patterns, evolution and long trends in numerical, time series or labelled data. The objective of this work is to develop a computational system capable of automatically generate linguistic descriptions in time series data containing labelled data, not only of the whole series, but also on the differences between subsets of the data. For this purpose we propose a new type of differential summaries, based on a numerical criterion assessing the behaviour of the summary on each subset of interest. Furthermore, this paper proposes an extension of linguistic summaries to provide temporal and categorical contextualisation. This is of particular interest in healthcare to detect differences related to a condition or illness as well as the effectiveness of the administered treatment.