Towards an efficient generation of linguistic summaries of time series using a degree of focus

We present an approach to a more efficient generation of linguistic summaries using the traditional degree of truth and a degree of focus based mechanism for discarding nonpromising summaries. We use the approach to derive linguistic data summaries to subsume the past performance of an investment (mutual) fund, and then present numerical results on the efficiency of the truncation process proposed. It turns out that the mechanism proposed is efficient and makes possible to disregards between 75% to 99% possible summaries.