Process-To-Text: A Framework for the Quantitative Description of
  Processes in Natural Language
Yago Fontenla-Seco Alberto Bugarín-Diz Manuel Lama
In this paper we present the Process-To-Text (P2T) framework for the
automatic generation of textual descriptive explanations of processes. P2T
integrates three AI paradigms: process mining for extracting temporal and
structural information from a process, fuzzy linguistic protoforms for
modelling uncertain terms, and natural language generation for building the
explanations. A real use-case in the cardiology domain is presented, showing
the potential of P2T for providing natural language explanations addressed to
specialists.