When tabular data cannot be directly mined, due to their large size or for privacy reasons, their summary may still be available for analysis. Fuzzy linguistic summaries are composed of sentences describ- ing a multivariate data distribution using, possibly personalized, terms taken from a fuzzy vocabulary. This paper introduces an algorithmic solution to the comparison of summaries so as to provide users with a linguistic description of the data changes between datasets to be com- pared. A first strategy processes exhaustive summaries containing one sentence for each of the subspaces that can be formed using terms from the vocabulary. A second strategy is proposed for condensed summaries, that involve informative sentences only. Experimentation conducted on artificial datasets confirm the relevance of this second strategy in terms of computational cost and data changes that can be tracked.