@inproceedings{li-etal-2026-dosa,
title = "{DOSA}: A Tree-Guided, Self-Regressive Framework for Long Document Structure Analysis",
author = "Li, Bohou and
Sowell, Benjamin and
Shah, Mehul A and
Lindblad, Mark and
Lindeman, Henry",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.32/",
pages = "670--682",
ISBN = "979-8-89176-439-2",
abstract = "In visually-rich documents, information is encoded not only in individual page objects such as tables, headers, and text blocks, but also in the structural relations among them, making document structure analysis fundamental to information retrieval and document understanding. However, accurately inferring such relations remains challenging in multi-page documents with long-range dependencies and heterogeneous layouts. To address this, we propose a tree-guided and self-regressive framework, termed $\textbf{DOcument Structure Analyzer (DOSA)}$, for inferring relations among page objects and reconstructing document-level semantic trees. DOSA processes documents chunk-by-chunk, fusing visual, textual, and layout features for each page object and predicting hierarchical and ordering relations. The predicted relations are used to incrementally construct a semantic tree, which is then leveraged as structural context to guide inference on subsequent chunks. Experimental results on five benchmarks demonstrate the effectiveness of DOSA, with improvements of up to 4 F1 points and 19 TEDS points on DocHieNet, the most challenging multi-page hierarchy benchmark."
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%0 Conference Proceedings
%T DOSA: A Tree-Guided, Self-Regressive Framework for Long Document Structure Analysis
%A Li, Bohou
%A Sowell, Benjamin
%A Shah, Mehul A.
%A Lindblad, Mark
%A Lindeman, Henry
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F li-etal-2026-dosa
%X In visually-rich documents, information is encoded not only in individual page objects such as tables, headers, and text blocks, but also in the structural relations among them, making document structure analysis fundamental to information retrieval and document understanding. However, accurately inferring such relations remains challenging in multi-page documents with long-range dependencies and heterogeneous layouts. To address this, we propose a tree-guided and self-regressive framework, termed DOcument Structure Analyzer (DOSA), for inferring relations among page objects and reconstructing document-level semantic trees. DOSA processes documents chunk-by-chunk, fusing visual, textual, and layout features for each page object and predicting hierarchical and ordering relations. The predicted relations are used to incrementally construct a semantic tree, which is then leveraged as structural context to guide inference on subsequent chunks. Experimental results on five benchmarks demonstrate the effectiveness of DOSA, with improvements of up to 4 F1 points and 19 TEDS points on DocHieNet, the most challenging multi-page hierarchy benchmark.
%U https://aclanthology.org/2026.aacl-long.32/
%P 670-682
Markdown (Informal)
[DOSA: A Tree-Guided, Self-Regressive Framework for Long Document Structure Analysis](https://aclanthology.org/2026.aacl-long.32/) (Li et al., AACL-IJCNLP 2026)
ACL
- Bohou Li, Benjamin Sowell, Mehul A Shah, Mark Lindblad, and Henry Lindeman. 2026. DOSA: A Tree-Guided, Self-Regressive Framework for Long Document Structure Analysis. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 670–682, Hengqin, China. Association for Computational Linguistics.