Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912065093894144 |
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| author | Li, Zichao He, Shaojie Liao, Meng Chen, Xuanang Lu, Yaojie Lin, Hongyu Lu, Yanxiong Han, Xianpei Sun, Le |
| author_facet | Li, Zichao He, Shaojie Liao, Meng Chen, Xuanang Lu, Yaojie Lin, Hongyu Lu, Yanxiong Han, Xianpei Sun, Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06802 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seg2Act: Global Context-aware Action Generation for Document Logical Structuring Li, Zichao He, Shaojie Liao, Meng Chen, Xuanang Lu, Yaojie Lin, Hongyu Lu, Yanxiong Han, Xianpei Sun, Le Computation and Language 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. |
| title | Seg2Act: Global Context-aware Action Generation for Document Logical Structuring |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.06802 |