On modeling vagueness and uncertainty in data-to-text systems through
  fuzzy sets
  Vagueness and uncertainty management is counted among one of the challenges
that remain unresolved in systems that generate texts from non-linguistic data,
known as data-to-text systems. In the last decade, work in fuzzy linguistic
summarization and description of data has raised the interest of using fuzzy
sets to model and manage the imprecision of human language in data-to-text
systems. However, despite some research in this direction, there has not been
an actual clear discussion and justification on how fuzzy sets can contribute
to data-to-text for modeling vagueness and uncertainty in words and
expressions. This paper intends to bridge this gap by answering the following
questions: What does vagueness mean in fuzzy sets theory? What does vagueness
mean in data-to-text contexts? In what ways can fuzzy sets theory contribute to
improve data-to-text systems? What are the challenges that researchers from
both disciplines need to address for a successful integration of fuzzy sets
into data-to-text systems? In what cases should the use of fuzzy sets be
avoided in D2T? For this, we review and discuss the state of the art of
vagueness modeling in natural language generation and data-to-text, describe
potential and actual usages of fuzzy sets in data-to-text contexts, and provide
some additional insights about the engineering of data-to-text systems that
make use of fuzzy set-based techniques.
