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

TL;DR

Seg2Act is introduced, an end-to-end, generation-based method for document logical structuring, revisiting logical structure extraction as an action generation task, given the text segments of a document.

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.

Figures (3)

• Figure 1: The illustration of document logical structuring task, which aims to transform text segments into a hierarchical tree structure containing the document's headings and paragraphs.
• Figure 2: A generation step of Seg2Act. The action generation model converts current text segments into actions to incrementally construct the document logical structure. A global context stack is maintained to enhance the model's global awareness, while the generated actions then being employed to update the stack.
• Figure 3: Results (F1-score of total nodes) for documents with different logical tree depths (a) and token lengths (b) on ChCatExt dataset.