Process Extraction from Text: state of the art and challenges for the
  future
  Automatic Process Discovery aims at developing algorithmic methodologies for
the extraction and elicitation of process models as described in data. While
Process Discovery from event-log data is a well established area, that has
already moved from research to concrete adoption in a mature manner, Process
Discovery from text is still a research area at an early stage of development,
which rarely scales to real world documents. In this paper we analyze, in a
comparative manner, reference state-of-the-art literature, especially for what
concerns the techniques used, the process elements extracted and the
evaluations performed. As a result of the analysis we discuss important
limitations that hamper the exploitation of recent Natural Language Processing
techniques in this field and we discuss fundamental limitations and challenges
for the future concerning the datasets, the techniques, the experimental
evaluations, and the pipelines currently adopted and to be developed in the
future.
