Elamur Research⌕
☰
home / papers / 2026 / MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents

MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents

Paper · 2026 · Shuyi Zhang, Zhenbin Chen, Shuting Li et al.

Summary

Most information extraction (IE) work targets single sentences or a narrow domain, because annotating long documents by hand is expensive. This paper introduces MMUIE, a document-level IE dataset that covers 34 domains. The authors built it with an automatic labeling pipeline that combines Wikidata, GPT-4o-mini and task-specific pretrained models such as spaCy. The pipeline runs in two stages. The first finds entities and groups their co-referring aliases. The second produces relation triples, including links that span several sentences.

The source texts are Wikipedia articles of 500–3,000 words. The training portion is weakly supervised, and the test portion was checked by humans. Sentence-level universal extractors, reasoning-capable LLMs and the LangExtract tool all score poorly on this benchmark.

The authors also fine-tuned an open-source LLM on the data to create DocUIE. It surpasses earlier generative state-of-the-art systems on Re-DocRED and DWIE. Training on many domains together helps more than training on one domain. The resource gives a basis for domain-aware IE over long texts, and the code, data and models are publicly released.

Key points

  • Dataset scale: MMUIE spans 34 domains with 723 entity types and 456 relation types. The weakly labeled training split contains 8,357 documents averaging about 1,081 words. The manually verified test split, as reported in Table 1, has 343 documents.
  • Entity typing: Labeling starts from 12 general spaCy types and is then expanded with fine-grained types obtained from the LLM and aligned with Wikidata.
  • DocUIE on Re-DocRED: It reaches a relation-extraction F1 of 60.5, compared with 53.84 for AutoRE.
  • DocUIE on DWIE: It also beats the previous best system, REXEL (65.8).
  • Training format: Each training instance asks for k = 8 target types, a value chosen by ablation.
  • Error analysis: Extraction quality falls noticeably as documents get longer. Rare domain-specific relations are a major source of difficulty, and removing them mainly raises recall.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Science and Technology of ChinaHefei · China145 works · 114,409 citations🎓Wuhan UniversityWuhan · China47 works · 1,246 citations🎓ShanghaiTech UniversityShanghai · China14 works · 1,974 citations🏛️Beijing Institute for General Artificial IntelligenceBeijing · China11 works · 633 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksBiochemistry, Genetics and Molecular Biology · 263 worksMolecular Biology · 260 works

🧭 Topics 1Research subjects and languages the work is about.Information extraction · 175 works

Cite

Cite as Shuyi Zhang, Zhenbin Chen, Shuting Li, Kewei Tu, Li Jing, Zixia Jia and Zilong Zheng. 2026. MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.334

@inproceedings{zhang2026mmuie,
  title = {{MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents}},
  author = {Shuyi Zhang, Zhenbin Chen, Shuting Li, Kewei Tu, Li Jing, Zixia Jia and Zilong Zheng},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.334},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.334}
}

References

55 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Nacime Bouziani, Shubhi Tyagi, Joseph Fisher, Jens Lehmann, and Andrea Pierleoni. 2024. Rexel: An end-to-end model for document-level relation extraction and entity linking. arXiv preprint arXiv:2404.12788. find
  2. Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo, and Roberto Navigli. 2023. Red FM : a filtered and multilingual relation extraction dataset. arXiv preprint arXiv:2306.09802. find
  3. John Dagdelen, Alexander Dunn, Sanghoon Lee, Nicholas Walker, Andrew S Rosen, Gerbrand Ceder, Kristin A Persson, and Anubhav Jain. 2024. Structured information extraction from scientific text with large language models. Nature communications, 15(1):1418.
  4. Gregor Donabauer and Udo Kruschwitz. 2025. A reproducibility study of graph-based legal case retrieval. arXiv preprint arXiv:2504.08400. — [arXiv:2504.08400](https://arxiv.org/abs/2504.08400) find
  5. Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl. 2018. T-rex: A large scale alignment of natural language with knowledge base triples. In Proceedings of the Eleventh International Conference on Language Resources and Evaluati…
  6. Yi Feng, Chuanyi Li, and Vincent Ng. 2024. Legal case retrieval: A survey of the state of the art. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6472–6485. find
  7. Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, and Xiang Ren. 2019. Collaborative policy learning for open knowledge graph reasoning. arXiv preprint arXiv:1909.00230. find
  8. John Giorgi, Gary Bader, and Bo Wang. 2022. A sequence-to-sequence approach for document-level relation extraction. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 10– 25, Dublin, Ireland. Association for Computational Linguistics. find
  9. Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, and Huajun Chen. 2024. Iepile: Unearthing large-scale schema-based information extraction corpus. arXiv preprint arXiv:2402.14710. — [arXiv:2402.14710](https://arxiv.org/abs/2402.14710) find
  10. Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yutao Zeng, Wenxuan Liu, Xiang Li, Pan Yang, Long Bai, J. Guo, and Xueqi Cheng. 2023. Retrieval-augmented code generation for universal information extraction. In Natural Language Processing and Chinese Computing. find
  11. Wenlong Hou, Weidong Zhao, Xianhui Liu, and Wenyan Guo. 2024. Knowledge-enriched prompt for low-resource named entity recognition. ACM Trans. Asian Low-Resour. Lang. Inf. Process., 23(5). find
  12. Quzhe Huang, Shibo Hao, Yuan Ye, Shengqi Zhu, Yansong Feng, and Dongyan Zhao. 2022. Does recommend-revise produce reliable annotations? an analysis on missing instances in docred. arXiv preprint arXiv:2204.07980. find
  13. Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, and Ruifeng Xu. 2023a. Set learning for generative information extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13043–13052. find
  14. Pere-Lluís Huguet Cabot and Roberto Navigli. 2021. REBEL: Relation extraction by end-to-end language generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2370– 2381, Punta Cana, Dominican Republic. Association for Computational Linguistics. find
  15. Junpeng Li, Zixia Jia, and Zilong Zheng. 2023b. Semi-automatic data enhancement for document-level relation extraction with distant supervision from large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5495–5505, Singapore. Associati… find
  16. Sarthak Jain, Madeleine Van Zuylen, Hannaneh Hajishirzi, and Iz Beltagy. 2020. Scirex: A challenge dataset for document-level information extraction. arXiv preprint arXiv:2005.00512.
  17. Rebii Jamal, Mounir Ourekouch, and Mohammed Erradi. 2025. UOREX: Towards uncertainty-aware open relation extraction. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pag… find
  18. Robin Jia, Cliff Wong, and Hoifung Poon. 2019. Document-level n-ary relation extraction with multiscale representation learning. ArXiv, abs/1904.02347. find
  19. Guochao Jiang, Ziqin Luo, Yuchen Shi, Dixuan Wang, Jiaqing Liang, and Deqing Yang. 2024. ToNER: Type-oriented named entity recognition with generative language model. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLIN… find
  20. Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. 2004. Estimating mutual information. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 69(6):066138. find
  21. Guozheng Li, Peng Wang, Jiajun Liu, Yikai Guo, Ke Ji, Ziyu Shang, and Zijie Xu. 2024a. Meta in-context learning makes large language models better zero and few-shot relation extractors. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI ’24. find
  22. Hao Li, Yubing Ren, Yanan Cao, Yingjie Li, Fang Fang, Zheng Lin, and Shi Wang. 2025a. Bridging the gap: Aligning language model generation with structured information extraction via controllable state transition. In Proceedings of the ACM on Web Conference 2025, pages 1811–1821. find
  23. Huahang Li, Shuangyin Li, Fei Hao, Chen Jason Zhang, Yuanfeng Song, and Lei Chen. 2024b. Booster: Leveraging large language models for enhancing entity resolution. In Companion Proceedings of the ACM Web Conference 2024, pages 1043–1046. find
  24. Peng Li, Tianxiang Sun, Qiong Tang, Hang Yan, Yuanbin Wu, Xuanjing Huang, and Xipeng Qiu. 2023c. CodeIE: Large code generation models are better few-shot information extractors. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages…
  25. Zhongqiu Li, Shiquan Wang, Ruiyu Fang, Mengjiao Bao, Zhenhe Wu, Shuangyong Song, Yongxiang Li, and Zhongjiang He. 2025b. Mr-uie: Multi-perspective reasoning with reinforcement learning for universal information extraction. arXiv preprint arXiv:2509.09082. — [arXiv:2509.09082](https://arxiv.org/abs/2509.09082) find
  26. Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Xiang Li, Zhilei Hu, et al. 2024c. Knowcoder: Coding structured knowledge into llms for universal information extraction. arXiv preprint arXiv:2403.07969.
  27. Xiao Ling and Daniel Weld. 2012. Fine-grained entity recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 26, pages 94–100. find
  28. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  29. Jie Lou, Yaojie Lu, Dai Dai, Wei Jia, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2023. Universal information extraction as unified semantic matching. In Proceedings of the AAAI conference on Artificial Intelligence, volume 37, pages 13318–13326. find
  30. Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, and Hua Wu. 2022. Unified structure generation for universal information extraction. arXiv preprint arXiv:2203.12277. find
  31. Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 2018. Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. arXiv preprint arXiv:1808.09602.
  32. Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, and Zhiyong Lu. 2022. Biored: a rich biomedical relation extraction dataset. Briefings in Bioinformatics, 23(5):bbac282. find
  33. Xuanfan Ni and Piji Li. 2023. Unified text structuralization with instruction-tuned language models. ArXiv, abs/2303.14956. — [arXiv:2303.14956](https://arxiv.org/abs/2303.14956) find
  34. Yingjie Niu, Lanxin Lu, Rian Dolphin, Valerio Poti, and Ruihai Dong. 2024. Evaluating financial relational graphs: Interpretation before prediction. In Proceedings of the 5th ACM International Conference on AI in Finance, pages 564–572. find
  35. Riccardo Orlando, Pere-Lluís Huguet Cabot, Edoardo Barba, and Roberto Navigli. 2024. ReLiK: Retrieve and LinK, fast and accurate entity linking and relation extraction on an academic budget. In Findings of the Association for Computational Linguistics: ACL 2024, pages 14114–14132, Bangkok, Thailand.… find
  36. Yilmazcan Ozyurt, Stefan Feuerriegel, and Ce Zhang. 2023. Document-level in-context few-shot relation extraction via pre-trained language models. find
  37. Giovanni Paolini, Ben Athiwaratkun, Jason Krone, JIE MA, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos Santos, Bing Xiang, and Stefano Soatto. 2021. Structured prediction as translation between augmented natural languages. find
  38. Oscar Sainz, Iker García-Ferrero, Rodrigo Agerri, Oier Lopez de Lacalle, German Rigau, and Eneko Agirre. 2023. Gollie: Annotation guidelines improve zero-shot information-extraction. arXiv preprint arXiv:2310.03668.
  39. Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, and Sharifah Mahani Aljunied. 2022. Revisiting docred– addressing the false negative problem in relation extraction. arXiv preprint arXiv:2205.12696.
  40. Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li, and Zi Huang. 2024a. Casegnn++: Graph contrastive learning for legal case retrieval with graph augmentation. arXiv preprint arXiv:2405.11791. — [arXiv:2405.11791](https://arxiv.org/abs/2405.11791) find
  41. Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li, and Zi Huang. 2024b. Casegnn: Graph neural networks for legal case retrieval with text-attributed graphs. In European Conference on Information Retrieval, pages 80–95. Springer. find
  42. Hongbo Wang, Weimin Xiong, Yifan Song, Dawei Zhu, Yu Xia, and Sujian Li. 2023a. Docred-fe: a document-level fine-grained entity and relation extraction dataset. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE. find
  43. Jianian Wang, Sheng Zhang, Yanghua Xiao, and Rui Song. 2021. A review on graph neural network methods in financial applications. arXiv preprint arXiv:2111.15367. — [arXiv:2111.15367](https://arxiv.org/abs/2111.15367) find
  44. Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, et al. 2023b. Instructuie: Multi-task instruction tuning for unified information extraction. arXiv preprint arXiv:2304.08085.
  45. Xinglin Xiao, Yijie Wang, Nan Xu, Yuqi Wang, Hanxuan Yang, Minzheng Wang, Yin Luo, Lei Wang, Wenji Mao, and Daniel Zeng. 2023. Yayi-uie: A chat-enhanced instruction tuning framework for universal information extraction. ArXiv, abs/2312.15548. — [arXiv:2312.15548](https://arxiv.org/abs/2312.15548) find
  46. Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang, and Enhong Chen. 2024. Large language models for generative information extraction: A survey. Frontiers of Computer Science, 18(6):186357.
  47. Lilong Xue, Dan Zhang, Yuxiao Dong, and Jie Tang. 2024. Autore: document-level relation extraction with large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 211–220.
  48. Faren Yan, Peng Yu, and Xin Chen. 2024. Ltner: Large language model tagging for named entity recognition with contextualized entity marking. In Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XIX, page 399–411, Berlin, Heidelberg.… find
  49. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxin Yang, Jingren Zhou,… — Qwen3 Technical Report
  50. Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. 2019. Docred: A large-scale document-level relation extraction dataset. arXiv preprint arXiv:1906.06127. find
  51. Haoze Yu, Haisheng Li, Dianhui Mao, and Qiang Cai. 2020. A relationship extraction method for domain knowledge graph construction. World Wide Web, 23(2):735–753. find
  52. Klim Zaporojets, Johannes Deleu, Chris Develder, and Thomas Demeester. 2021. Dwie: An entity-centric dataset for multi-task document-level information extraction. Information Processing & Management, 58(4):102563. find
  53. Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou, and Lei Zou. 2023a. LLMaAA: Making large language models as active annotators. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13088–13103, Singapore. Association for Computational Linguistics. find
  54. Ruoyu Zhang, Yanzeng Li, and Lei Zou. 2023b. A novel table-to-graph generation approach for document-level joint entity and relation extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10853–10865. find
  55. Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, and Xindong Wu. 2023. A comprehensive survey on automatic knowledge graph construction. ACM Computing Surveys, 56(4):1–62. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/zhang-2026-mmuie-massive-multi-domain-universal/