File size: 5,850 Bytes
35f4e28 14fa756 35f4e28 14fa756 35f4e28 14fa756 6da6f18 35f4e28 fe4b3bc 35f4e28 8f045d5 35f4e28 8f045d5 35f4e28 14fa756 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | ---
language:
- en
license: mit
size_categories:
- n<1K
pretty_name: ArcBench ML Conference Oral Paper-Presentation Benchmark
task_categories:
- other
tags:
- machine-learning
- academic-papers
- slides
- multimodal
- benchmark
- nlp
- computer-vision
- oral-presentations
---
# ArcBench: ML Conference Oral Paper-Presentation Benchmark
This benchmark is from the paper **[Narrative-Driven Paper-to-Slide Generation via ArcDeck](https://huggingface.co/papers/2604.11969)**.
A curated benchmark dataset of **100 oral presentation papers** from top-tier machine learning conferences (CVPR, ICCV, ICLR, ICML, NeurIPS), spanning 2022–2025. Each entry includes the full paper PDF, presentation slides PDF, and rich metadata.
[](https://arxiv.org/abs/2604.11969)
[](https://arcdeck.org/)
[](https://huggingface.co/datasets/ArcDeck/ArcBench)
[](https://github.com/RehgLab/ArcDeck)
---
## Dataset Summary
This benchmark is designed to support research on multimodal document understanding, slide generation, paper-to-slide alignment, and LLM evaluation tasks. Papers were selected from oral presentations only — the highest-quality subset of each conference — and filtered to ensure rich content (≥3 figures, ≥3 tables) and availability of both the original paper PDF and presentation slides.
---
## Dataset Structure
### Files
```
benchmark.csv # Metadata for all 100 papers
papers/ # 100 original full-length paper PDFs
└── paper{i}_{Title}_{Conference}_{Year}.pdf
slides/ # 100 presentation slide PDFs
└── slide{i}_{Title}_{Conference}_{Year}.pdf
```
### Metadata Fields (`benchmark.csv`)
| Column | Type | Description |
|--------|------|-------------|
| `Paper Title` | string | Full paper title |
| `Year` | int | Publication year (2022–2025) |
| `Conference` | string | Conference name (CVPR, ICCV, ICLR, ICML, NeurIPS) |
| `Presentation Type` | string | Always `Oral` in this benchmark |
| `Number of Figures` | int | Number of figures in the paper |
| `Number of Equations` | int | Number of equations in the paper |
| `Number of Tables` | int | Number of tables in the paper |
| `Appendix` | string | Whether paper has an appendix (`Yes`/`No`) |
| `Slide Animations` | string | Notes on slide animations, if any |
| `Character_Count` | int | Total character count of the paper (extracted via PDF) |
| `Number_of_Slides` | int | Number of pages/slides in the slide PDF |
| `Topics` | string | Semicolon-separated LLM-extracted research topics |
### Naming Convention
Files are named as `{type}{index}_{CleanTitle}_{Conference}_{Year}.pdf` where:
- `index` is 0-based, consistent across `papers/` and `slides/` for matched pairs
- `CleanTitle` has special characters removed and spaces replaced by underscores (max 100 chars)
---
## Dataset Statistics
### Distribution by Conference
| Conference | Papers |
|------------|--------|
| ICML | 51 |
| ICLR | 31 |
| NeurIPS | 12 |
| ICCV | 4 |
| CVPR | 2 |
### Distribution by Year
| Year | Papers |
|------|--------|
| 2022 | 15 |
| 2023 | 15 |
| 2024 | 26 |
| 2025 | 44 |
### Content Statistics
| Metric | Mean | Min | Max |
|--------|------|-----|-----|
| Figures per paper | 6.0 | 3 | 18 |
| Tables per paper | 5.3 | 3 | — |
| Slides per paper | 27.5 | 8 | 85 |
| Characters per paper | 50,411 | — | — |
- **92%** of papers include an appendix
- **100%** are oral presentations
### Top Research Topics
Extracted via GPT-4o-mini from paper abstracts:
> Contrastive Learning · Graph Neural Networks · Causal Inference · Multimodal Large Language Models · Federated Learning · Sampling Efficiency · Reinforcement Learning · Diffusion Models · Self-Supervised Learning · Vision-Language Models
---
## Selection Criteria
Papers were selected using the following filters applied to a broader 994-paper dataset:
- **Presentation type:** Oral only
- **Minimum figures:** ≥ 3
- **Minimum tables:** ≥ 3
- **Original paper available:** Must have the full (non-anonymized) version
- **Balanced sampling:** Proportional stratified sampling across year × conference to reach exactly 100 papers
---
## Intended Uses
This dataset is suited for:
- **Slide generation / paper-to-slide summarization**: Given `papers/`, generate slides comparable to `slides/`
- **Slide-grounded QA**: Answer questions about a paper using its slides as context
- **Cross-modal retrieval**: Match papers to their corresponding slides
- **LLM evaluation**: Benchmark LLM understanding of dense scientific documents
- **Multimodal document analysis**: Study relationships between figures, tables, equations, and slide content
---
## Source
Papers were collected from official proceedings of:
- [ICML](https://icml.cc) (2022–2025)
- [ICLR](https://iclr.cc) (2024–2025)
- [NeurIPS](https://neurips.cc) (2022–2025)
- [CVPR](https://cvpr.thecvf.com) (2024–2025)
- [ICCV](https://iccv2023.thecvf.com) (2025)
---
## Citation
If you use this dataset in your research, please cite:
```bibtex
@article{ozden2026arcdeck,
title = {Narrative-Driven Paper-to-Slide Generation via ArcDeck},
author = {Ozden, Tarik Can and VS, Sachidanand and Horoz, Furkan
and Kara, Ozgur and Kim, Junho and Rehg, James M.},
journal = {arXiv preprint arXiv:2604.11969},
year = {2026}
}
```
---
## License
MIT.
Individual paper and slide PDFs remain under their original authors' copyright. Please consult each paper's license before redistribution. |