@inproceedings{li-etal-2024-seg2act,
title = "{S}eg2{A}ct: Global Context-aware Action Generation for Document Logical Structuring",
author = "Li, Zichao and
He, Shaojie and
Liao, Meng and
Chen, Xuanang and
Lu, Yaojie and
Lin, Hongyu and
Lu, Yanxiong and
Han, Xianpei and
Sun, Le",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1003",
pages = "18077--18088",
abstract = "Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short in handling the complexity and the variability of lengthy documents. To address these issues, we introduce Seg2Act, an end-to-end, generation-based method for document logical structuring, revisiting logical structure extraction as an action generation task. Specifically, given the text segments of a document, Seg2Act iteratively generates the action sequence via a global context-aware generative model, and simultaneously updates its global context and current logical structure based on the generated actions. Experiments on ChCatExt and HierDoc datasets demonstrate the superior performance of Seg2Act in both supervised and transfer learning settings.",
}
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<abstract>Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short in handling the complexity and the variability of lengthy documents. To address these issues, we introduce Seg2Act, an end-to-end, generation-based method for document logical structuring, revisiting logical structure extraction as an action generation task. Specifically, given the text segments of a document, Seg2Act iteratively generates the action sequence via a global context-aware generative model, and simultaneously updates its global context and current logical structure based on the generated actions. Experiments on ChCatExt and HierDoc datasets demonstrate the superior performance of Seg2Act in both supervised and transfer learning settings.</abstract>
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%0 Conference Proceedings
%T Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
%A Li, Zichao
%A He, Shaojie
%A Liao, Meng
%A Chen, Xuanang
%A Lu, Yaojie
%A Lin, Hongyu
%A Lu, Yanxiong
%A Han, Xianpei
%A Sun, Le
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-seg2act
%X Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short in handling the complexity and the variability of lengthy documents. To address these issues, we introduce Seg2Act, an end-to-end, generation-based method for document logical structuring, revisiting logical structure extraction as an action generation task. Specifically, given the text segments of a document, Seg2Act iteratively generates the action sequence via a global context-aware generative model, and simultaneously updates its global context and current logical structure based on the generated actions. Experiments on ChCatExt and HierDoc datasets demonstrate the superior performance of Seg2Act in both supervised and transfer learning settings.
%U https://aclanthology.org/2024.emnlp-main.1003
%P 18077-18088
Markdown (Informal)
[Seg2Act: Global Context-aware Action Generation for Document Logical Structuring](https://aclanthology.org/2024.emnlp-main.1003) (Li et al., EMNLP 2024)
ACL
- Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, and Le Sun. 2024. Seg2Act: Global Context-aware Action Generation for Document Logical Structuring. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18077–18088, Miami, Florida, USA. Association for Computational Linguistics.