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---
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license: mit
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dataset_info:
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features:
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- name: instance_id
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dtype: string
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- name: doc_type
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dtype: string
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- name: source
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dtype: string
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- name: url
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dtype: string
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- name: edu_pred_input
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dtype: string
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- name: ground_truth
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dtype: string
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splits:
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- name: test
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num_bytes: 32221618
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num_examples: 248
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download_size: 6102151
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dataset_size: 32221618
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configs:
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- config_name: default
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data_files:
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- split: test
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path: test/data.parquet
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---
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# Dataset Card for StructBench
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## Dataset Summary
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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.
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To ensure reliable ground truth, all documents were:
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- Parsed and sentence-segmented
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- Manually annotated by human experts for discourse structure
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In addition to the structured annotations, raw Web pages and PDF files are included.
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## Dataset Structure
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- **test/**: evaluation-only split
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- **data.parquet**: data samples
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- **raw_pdf_files/**: original PDF files
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- **raw_web_htmls/**: original WEB htmls
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## Tasks
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- Discouse Analysis
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- Document Structure Parsing
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- Document Understanding
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## Usage
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Clone the dataset:
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```bash
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git clone https://huggingface.co/datasets/deeplang-ai/StructBench
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```
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Or load with Hugging Face datasets library:
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```python
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from datasets import load_dataset
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dataset = load_dataset("deeplang-ai/StructBench", split="test")
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```
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## Citation
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```bibtex
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@misc{zhou2025contextedusfaithfulstructured,
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title={From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition},
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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},
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year={2025},
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eprint={2512.14244},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2512.14244},
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}
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```
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