  The generation of natural language text from data series gained renewed
interest among AI research goals. Not surprisingly, the few proposals in the
state of the art are based on training some system, in order to produce a text
that describes and that is coherent to the data provided as input. Main
challenges of such approaches are the proper identification of "what" to say
(the key descriptive elements to be addressed in the data) and "how" to say:
the correspondence and accuracy between data and text, the presence of
contradictions/redundancy in the text, the control of the amount of synthesis.
  This paper presents a framework that is compliant with xAI requirements. In
particular we model ASP/Python programs that enable an explicit control of
accuracy errors and amount of synthesis, with proven optimal solutions. The
text description is hierarchically organized, in a top-down structure where
text is enriched with further details, according to logic rules. The generation
of natural language descriptions' structure is also managed by logic rules.
