Skip to content

Latest commit

 

History

History
189 lines (140 loc) · 11.4 KB

README.md

File metadata and controls

189 lines (140 loc) · 11.4 KB

arXiv GitHub issues PRs Welcome

DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models

We present DocGenome, a structured document dataset constructed by annotating 500K scientific documents from 153 disciplines in the arXiv open-access community, using our custom auto-labeling pipeline DocParser. DocGenome features four characteristics:

    1. Completeness: It is the first dataset to structure data from all modalities including 13 layout attributes along with their \LaTeX\ source codes.
    1. Logicality: It provides 6 logical relationships between different entities within each scientific document.
    1. Diversity: It covers various document-oriented tasks, including document classification, visual grounding, document layout detection, document transformation, open-ended single-page QA and multi-page QA.
    1. Correctness: It undergoes rigorous quality control checks conducted by a specialized team.

Release

File Structure

Please refer to Dataset_Details_README for the detalied explanation about the different file structures in DocGenome.

DocGenome Benchmark Introduction

Datasets # Discipline # Category of Units # Pages in Train-set # Pages in Test-set # Task # Used Metric Publication Entity Relations
DocVQA - N/A 11K 1K 1 2 1960-2000
DocLayNet - 11 80K 8K 1 1 -
DocBank - 13 0.45M 50K 3 1 2014-2018
PubLayNet - 5 0.34M 12K 1 1 -
VRDU - 10 7K 3K 3 1 -
DUDE - N/A 20K 6K 3 3 1860-2022
D^4LA - 27 8K 2K 1 3 -
Fox Benchmark - 5 N/A (No train-set) 0.2K 3 5 -
ArXivCap 32 N/A 6.4M* N/A 4 3 -
DocGenome (ours) 153 13 6.8M 9K 7 7 2007-2022

👇🏻DocGenome-train Download

We provide 8 subsets of DocGenome-train for downloading:

Data Download

Definition of relationships between component units

DocGenome contains 4 level relation types and 2 cite relation types, as shown in the following table:

Name Description Example
Identical Two blocks share the same source code. Cross-column text; Cross-page text.
Title adjacent The two titles are adjacent. (\section{introduction}, \section{method})
Subordinate One block is a subclass of another block. (\section{introduction}, paragraph within Introduction)
Non-title adjacent The two text or equation blocks are adjacent. (Paragraph 1, Paragraph 2)
Explicitly-referred One block refers to another block via footnote, reference, etc. (As shown in \ref{Fig: 5} ..., Figure 5)
Implicitly-referred The caption block refers to the corresponding float environment. (Table Caption 1, Table 1)

Attribute of component units

DocGenome has 13 attributes of component units, which can be categorized into two classes

  • 1) Fixed-form units, including Text, Title, Abstract, etc., which are characterized by sequential reading and hierarchical relationships readily discernible from the list obtained in Stage-two of the designed DocParser.
  • 2) Floating-form units, including Table, Figure, etc., which establish directional references to fixed-form units through commands like \texttt{\textbackslash ref} and \texttt{\textbackslash label}.
Index Category Notes
0 Algorithm
1 Caption Titles of Images, Tables, and Algorithms
2 Equation
3 Figure
4 Footnote
5 List
7 Table
8 Text
9 Text-EQ Text block with inline equations
10 Title Section titles
12 PaperTitle
13 Code
14 Abstract

Types of disciplines

Page distribution of DocGenome. 20% of documents are five pages or fewer, 50% are ten pages or fewer, and 80% are nineteen pages or fewer.

Page Distribution

Distribution of secondary disciplines in our DocGenome. The count on the x-axis represents the number of documents, and documents from the same primary discipline are marked with the same color.

Discipline Distribution

DocParser: A Cutting-edge Auto-labeling Pipeline

Visualizations

Visual Example One of annotations in DocGenome
Visual Example One of annotations in DocGenome
Visual examples of document-oriented tasks in DocGenome

Citation

If you find our work useful in your research, please consider citing Fox:

@article{xia2024docgenome,
  title={DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models},
  author={Xia, Renqiu and Mao, Song and Yan, Xiangchao and Zhou, Hongbin and Zhang, Bo and Peng, Haoyang and Pi, Jiahao and Fu, Daocheng and Wu, Wenjie and Ye, Hancheng and others},
  journal={arXiv preprint arXiv:2406.11633},
  year={2024}
}