# Document Automation Architectures and Technologies: A Survey

arXiv: https://arxiv.org/abs/2109.11603 · HTML: https://mlsanity.com/paper/2109.11603/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 119 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge](https://mlsanity.com/paper/1901.07931/) (2019)
- [GPT-too: A language-model-first approach for AMR-to-text generation](https://mlsanity.com/paper/2005.09123/) (2020)
- [Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models](https://mlsanity.com/paper/2106.01623/) (2021)
- [Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings](https://mlsanity.com/paper/1606.05491/) (2016)
- [Context-aware Natural Language Generation with Recurrent Neural Networks](https://mlsanity.com/paper/1611.09900/) (2016)
- [Tax Knowledge Graph for a Smarter and More Personalized TurboTax](https://mlsanity.com/paper/2009.06103/) (2020)
- [Generative Concatenative Nets Jointly Learn to Write and Classify Reviews](https://mlsanity.com/paper/1511.03683/) (2015)

## Built on by (most cited first, 3 of 3)

- [LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation](https://mlsanity.com/paper/2403.16295/) (2024)
- [Document Automation Architectures: Updated Survey in Light of Large Language Models](https://mlsanity.com/paper/2308.09341/) (2023)
- [AiBAT: Artificial Intelligence/Instructions for Build, Assembly, and Test](https://mlsanity.com/paper/2410.02955/) (2024)

## Abstract (arXiv)

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.