| --- |
| 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. |
