Doctopus: A System for Budget-aware Structural Data Extraction from Unstructured Documents
Summary: Doctopus blends LLM-based attribute extraction with non-LLM methods under a budget-aware optimizer for structured data in unstructured documents. Chunking narrows to relevant content, estimates per-attr strategy quality, and budget-aware picks the optimal approach. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yuanhao Zhong (Beijing Institute of Technology)
- 2. Yuhao Deng (Beijing Institute of Technology)
- 3. Chengliang Chai (Beijing Institute of Technology)
- 4. Ruixin Gu (Beijing Institute of Technology)
- 5. Ye Yuan (Beijing Institute of Technology)
- 6. Guoren Wang (Beijing Institute of Technology)
- 7. Lei Cao (University of Arizona)
BibTeX Citation
@inproceedings{zhong_sigmod25,
title = {{Doctopus: A System for Budget-aware Structural Data Extraction from Unstructured Documents}},
author = {Zhong, Yuanhao and Deng, Yuhao and Chai, Chengliang and Gu, Ruixin and Yuan, Ye and Wang, Guoren and Cao, Lei},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725103},
url = {https://dl.acm.org/doi/10.1145/3722212.3725103},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 713 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB | 0.00014672521 |
| 1,728 | Structured Querying of Web Text: A Technical Challenge | 2007 | CIDR | 9.9098087e-05 |
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