# μgat: Improving Single-Page Document Parsing by Providing Multi-Page Context arXiv: https://arxiv.org/abs/2408.15646 · HTML: https://mlsanity.com/paper/2408.15646/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 39 references that are in the map. - First posted on arXiv 28 Aug 2024. ## Builds on (most cited first, 30 of 33) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020) - [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021) - [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022) - [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019) - [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022) - [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020) - [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021) - [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022) - [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019) - [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019) - [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021) - [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023) - [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015) - [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020) - [DiT: Self-supervised Pre-training for Document Image Transformer](https://mlsanity.com/paper/2203.02378/) (2022) - [Unifying Vision, Text, and Layout for Universal Document Processing](https://mlsanity.com/paper/2212.02623/) (2022) - [Document Understanding Dataset and Evaluation (DUDE)](https://mlsanity.com/paper/2305.08455/) (2023) - [Hierarchical multimodal transformers for Multi-Page DocVQA](https://mlsanity.com/paper/2212.05935/) (2022) - [KOSMOS-2.5: A Multimodal Literate Model](https://mlsanity.com/paper/2309.11419/) (2023) - [End-to-end Document Recognition and Understanding with Dessurt](https://mlsanity.com/paper/2203.16618/) (2022) - [LaTr: Layout-Aware Transformer for Scene-Text VQA](https://mlsanity.com/paper/2112.12494/) (2021) - [Focus Anywhere for Fine-grained Multi-page Document Understanding](https://mlsanity.com/paper/2405.14295/) (2024) - [DocFormerv2: Local Features for Document Understanding](https://mlsanity.com/paper/2306.01733/) (2023) - [GRAM: Global Reasoning for Multi-Page VQA](https://mlsanity.com/paper/2401.03411/) (2024) - [CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data](https://mlsanity.com/paper/2304.14953/) (2023) ## Built on by (most cited first, 7 of 7) - [Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence](https://mlsanity.com/paper/2601.12318/) (2026) - [AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine](https://mlsanity.com/paper/2505.01435/) (2025) - [Autoregressive Styled Text Image Generation, but Make it Reliable](https://mlsanity.com/paper/2510.23240/) (2025) - [Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?](https://mlsanity.com/paper/2508.09936/) (2025) - [Shifting the Breaking Point of Flow Matching for Multi-Instance Editing](https://mlsanity.com/paper/2602.08749/) (2026) - [PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing](https://mlsanity.com/paper/2606.07661/) (2026) - [Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms](https://mlsanity.com/paper/2607.21780/) (2026) ## Abstract (arXiv) Regesta are catalogs of summaries of other documents and, in some cases, are the only source of information about the content of such full-length documents. For this reason, they are of great interest to scholars in many social and humanities fields. In this work, we focus on Regesta Pontificum Romanum, a large collection of papal registers. Regesta are visually rich documents, where the layout is as important as the text content to convey the contained information through the structure, and are inherently multi-page documents. Among Digital Humanities techniques that can help scholars efficiently exploit regesta and other documental sources in the form of scanned documents, Document Parsing has emerged as a task to process document images and convert them into machine-readable structured representations, usually markup language. However, current models focus on scientific and business documents, and most of them consider only single-paged documents. To overcome this limitation, in this work, we propose μgat, an extension of the recently proposed Document parsing Nougat architecture, which can handle elements spanning over the single page limits. Specifically, we adapt Nougat to process a larger, multi-page context, consisting of the previous and the following page, while parsing the current page. Experimental results, both qualitative and quantitative, demonstrate the effectiveness of our proposed approach also in the case of the challenging Regesta Pontificum Romanorum.