Document Automation Architectures: Updated Survey in Light of Large Language Models
It addresses the lack of comprehensive academic reviews in document automation, potentially benefiting researchers and practitioners by clarifying the field and suggesting future directions.
This paper surveys academic research on document automation architectures, providing a comprehensive review and clearer definition of the field, and identifies state-of-the-art technologies and new research opportunities in light of generative AI and 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.