2 research outputs found
Document Automation Architectures: Updated Survey in Light of Large Language Models
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 creating and 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
generative AI and large language models.Comment: The current paper is the updated version of an earlier survey on
document automation [Ahmadi Achachlouei et al. 2021]. Updates in the current
paper are as follows: We shortened almost all sections to reduce the size of
the main paper (without references) from 28 pages to 10 pages, added a review
of selected papers on large language models, removed certain sections and
most of diagrams. arXiv admin note: substantial text overlap with
arXiv:2109.1160