Document Automation Architectures: Updated Survey in Light of Large Language Models

TL;DR

This survey reviews academic research on document automation architectures, highlighting recent advances with large language models and generative AI, and identifies future research directions in the field.

Contribution

It provides a comprehensive review of academic DA architectures, clarifies definitions, and explores new research opportunities with recent AI advancements.

Findings

Identifies key DA architectures and technologies in academic research.

Highlights the impact of large language models on DA.

Suggests future research directions integrating generative AI.

Abstract

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.