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---
license: mit
dataset_info:
features:
- name: instance_id
dtype: string
- name: doc_type
dtype: string
- name: source
dtype: string
- name: url
dtype: string
- name: edu_pred_input
dtype: string
- name: ground_truth
dtype: string
splits:
- name: test
num_bytes: 32221618
num_examples: 248
download_size: 6102151
dataset_size: 32221618
configs:
- config_name: default
data_files:
- split: test
path: test/data.parquet
---
# Dataset Card for StructBench
## Dataset Summary
StructBench is a benchmark for evaluating fine-grained document structure analysis. It provides a high-quality test set of 248 documents in diverse formats, including 203 Web pages and 47 PDFs.
To ensure reliable ground truth, all documents were:
- Parsed and sentence-segmented
- Manually annotated by human experts for discourse structure
In addition to the structured annotations, raw Web pages and PDF files are included.
## Dataset Structure
- **test/**: evaluation-only split
- **data.parquet**: data samples
- **raw_pdf_files/**: original PDF files
- **raw_web_htmls/**: original WEB htmls
## Tasks
- Discouse Analysis
- Document Structure Parsing
- Document Understanding
## Usage
Clone the dataset:
```bash
git clone https://huggingface.co/datasets/deeplang-ai/StructBench
```
Or load with Hugging Face datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("deeplang-ai/StructBench", split="test")
```
## Citation
```bibtex
@misc{zhou2025contextedusfaithfulstructured,
title={From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition},
author={Yiqing Zhou and Yu Lei and Shuzheng Si and Qingyan Sun and Wei Wang and Yifei Wu and Hao Wen and Gang Chen and Fanchao Qi and Maosong Sun},
year={2025},
eprint={2512.14244},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.14244},
}
```
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