metadata
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:
git clone https://huggingface.co/datasets/deeplang-ai/StructBench
Or load with Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("deeplang-ai/StructBench", split="test")
Citation
@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},
}