  This paper surveys the current state of the art in document automation (DA).
The objective of DA is to reduce the manual effort during the generation of
documents by automatically integrating input from different sources and
assembling documents conforming to defined templates. There have been reviews
of commercial solutions of DA, particularly in the legal domain, but to date
there has been no comprehensive review of the academic research on DA
architectures and technologies. The current survey of DA reviews the academic
literature and provides a clearer definition and characterization of DA and its
features, identifies state-of-the-art DA architectures and technologies in
academic research, and provides ideas that can lead to new research
opportunities within the DA field in light of recent advances in artificial
intelligence and deep neural networks.
