Seg2Act: Global Context-aware Action Generation for Document Logical Structuring

• URL: http://arxiv.org/abs/2410.06802v1
• Date: Wed, 9 Oct 2024 11:58:40 GMT
• Title: Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
• Authors: Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, Le Sun,
• Abstract summary: We introduce Seg2Act, an end-to-end, generation-based method for document logical structuring. Seg2Act iteratively generates the action sequence via a global context-aware generative model, and simultaneously updates its global context and current logical structure. Experiments on ChCatExt and HierDoc datasets demonstrate the superior performance of Seg2Act in both supervised and transfer learning settings.
• Score: 45.55145491566147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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