@inproceedings{li-etal-2025-readoc,
title = "{READ}oc: A Unified Benchmark for Realistic Document Structured Extraction",
author = "Li, Zichao and
Abulaiti, Aizier and
Lu, Yaojie and
Chen, Xuanang and
Zheng, Jia and
Lin, Hongyu and
Han, Xianpei and
Jiang, Shanshan and
Dong, Bin and
Sun, Le",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1128/",
doi = "10.18653/v1/2025.findings-acl.1128",
pages = "21889--21905",
ISBN = "979-8-89176-256-5",
abstract = "Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequate, significantly hindering the field{'}s advancement. This problem is largely attributed to existing benchmark paradigms, which exhibit fragmented and localized characteristics. To offer a thorough evaluation of DSE systems, we introduce a novel benchmark named READoc, which defines DSE as a realistic task of converting unstructured PDFs into semantically rich Markdown. The READoc dataset is derived from 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo. In addition, we develop a DSE Evaluation S$^3$uite comprising Standardization, Segmentation and Scoring modules, to conduct a unified evaluation of state-of-the-art DSE approaches. By evaluating a range of pipeline tools, expert visual models, and general Vision-Language Models, we identify the gap between current work and the unified, realistic DSE objective for the first time. We aspire that READoc will catalyze future research in DSE, fostering more comprehensive and practical solutions."
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%0 Conference Proceedings
%T READoc: A Unified Benchmark for Realistic Document Structured Extraction
%A Li, Zichao
%A Abulaiti, Aizier
%A Lu, Yaojie
%A Chen, Xuanang
%A Zheng, Jia
%A Lin, Hongyu
%A Han, Xianpei
%A Jiang, Shanshan
%A Dong, Bin
%A Sun, Le
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F li-etal-2025-readoc
%X Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequate, significantly hindering the field’s advancement. This problem is largely attributed to existing benchmark paradigms, which exhibit fragmented and localized characteristics. To offer a thorough evaluation of DSE systems, we introduce a novel benchmark named READoc, which defines DSE as a realistic task of converting unstructured PDFs into semantically rich Markdown. The READoc dataset is derived from 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo. In addition, we develop a DSE Evaluation S³uite comprising Standardization, Segmentation and Scoring modules, to conduct a unified evaluation of state-of-the-art DSE approaches. By evaluating a range of pipeline tools, expert visual models, and general Vision-Language Models, we identify the gap between current work and the unified, realistic DSE objective for the first time. We aspire that READoc will catalyze future research in DSE, fostering more comprehensive and practical solutions.
%R 10.18653/v1/2025.findings-acl.1128
%U https://aclanthology.org/2025.findings-acl.1128/
%U https://doi.org/10.18653/v1/2025.findings-acl.1128
%P 21889-21905
Markdown (Informal)
[READoc: A Unified Benchmark for Realistic Document Structured Extraction](https://aclanthology.org/2025.findings-acl.1128/) (Li et al., Findings 2025)
ACL
- Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, and Le Sun. 2025. READoc: A Unified Benchmark for Realistic Document Structured Extraction. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21889–21905, Vienna, Austria. Association for Computational Linguistics.