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The large available amount of non-structured texts that be- long to different domains such as healthcare (e.g. medical records), justice (e.g. laws, declarations), insurance (e.g. declarations), etc. increases the effort required for the analysis of information in a decision making pro- cess. Different projects and tools have proposed strategies to reduce this complexity by classifying, summarizing or annotating the texts. Partic- ularly, text summary strategies have proven to be very useful to provide a compact view of an original text. However, the available strategies to generate these summaries do not fit very well within the domains that require take into consideration the temporal dimension of the text (e.g. a recent piece of text in a medical record is more important than a pre- vious one) and the profile of the person who requires the summary (e.g the medical specialization). To cope with these limitations this paper presents ”GReAT” a model for automatic summary generation that re- lies on natural language processing and text mining techniques to extract the most relevant information from narrative texts and discover new in- formation from the detection of related information. GReAT Model was implemented on software to be validated in a health institution where it has shown to be very useful to display a preview of the information about medical health records and discover new facts and hypotheses within the information. Several tests were executed such as Functional- ity, Usability and Performance regarding to the implemented software. In addition, precision and recall measures were applied on the results ob- tained through the implemented tool, as well as on the loss of information obtained by providing a text more shorter than the original.

Great Model: A Model for the Automatic Generation of Semantic Relations Between Text Summaries Based on the Relevant Information to the User