The automatic generation of fuzzy rules is a well-known task based either on the construction of a fuzzy decision tree or on the direct generation of fuzzy rules (e.g. association rules, gradual rules, or fuzzy summaries). In this paper, fuzzy rules obtained from fuzzy decision trees are compared to fuzzy summaries. In both approaches, the knowledge is presented to users using linguistic terms. In this framework, means to improve the interpretability of such fuzzy rules are necessary. The aim of this paper is to reinforce each method by taking advantage of the qualities of the other one. Several methods are proposed, mainly based on the use of fuzzy quantifiers, similarities and aggregations.