README.md

---
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 paper-slide deck link pairs** from top-tier machine learning conferences (CVPR, ICCV, ICLR, ICML, NeurIPS), spanning 2022–2025. Each entry provides rich metadata together with **links to the original paper PDF and presentation slides**, plus a script that downloads them all in one step.


[![arXiv](https://img.shields.io/badge/arXiv-2604.11969-b31b1b.svg)](https://arxiv.org/abs/2604.11969)
[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://arcdeck.org/)
[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/ArcDeck/ArcBench)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](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 + source links for all 100 papers
download_pdfs.py         # Downloads every paper/slide PDF from its source link
download_openreview.py   # Companion downloader for the OpenReview-hosted papers
papers/                  # Created by the download script: 100 paper PDFs
└── paper{i}_{Title}_{Conference}_{Year}.pdf
slides/                  # Created by the download script: 100 slide PDFs
└── slide{i}_{Title}_{Conference}_{Year}.pdf
```

The `papers/` and `slides/` folders are populated by running the download script 
(see [Downloading the PDFs](#downloading-the-pdfs)).

### 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 |
| `Paper PDF URL` | string | Link to the original paper PDF (arXiv, proceedings, OpenReview, …) |
| `Slides URL` | string | Link to the original presentation slides PDF |

### 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)

The download script reconstructs these exact filenames from `benchmark.csv`, so
file `i` always corresponds to row `i` of the metadata.

---

## Downloading the PDFs

You can download the PDFs from their original sources with the included script.

```bash
# Download all 100 papers + 100 slides into ./papers and ./slides
python download_pdfs.py
```

Useful options:

```bash
python download_pdfs.py --type slides          # slides only (or: papers, both)
python download_pdfs.py --indices 0,5,84        # just a few entries
python download_pdfs.py --limit 10              # first 10 entries
python download_pdfs.py --out /data/arcbench    # choose the output directory
python download_pdfs.py --workers 8             # more parallelism
python download_pdfs.py --force                 # re-download existing files
```

The script verifies every download is a real PDF, writes atomically, and
**skips files that are already present**, so it is safe to re-run to resume an
interrupted download. Any files it could not fetch are listed in
`download_failures.csv`.

### A note on versions

Links point to the **canonical, live source** for each work. For papers hosted
on arXiv this is the latest revision, which may differ slightly from the exact
PDF originally archived for the benchmark (updated figures, camera-ready edits,
etc.). The content is the same paper. Slide decks served by conference media
servers are typically byte-for-byte identical.

---

## 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

The benchmark **metadata** (`benchmark.csv`), the **source links**, and the
**download scripts** in this repository are released under the MIT license.