S eg2 A ct: Global Context-aware Action Generation for Document Logical Structuring (2024)

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.

## introduction document logical structuring is an essential task for document understanding, which aims to extract the underlying logical structure of documents @xcite @xcite @xcite @xcite . as shown in figure 1 , document logical structuring transforms a document into a hierarchical logical tree composing of headings and paragraphs. understanding a document's logical structure will benefit numerous downstream tasks, such as information retrieval @xcite , abstractive summarization @xcite , and assisting large language models in question answering over long structured documents @xcite . ## experiments this section evaluates seg2act by conducting experiments in both supervised learning and transfer learning settings. ## related work document logical structuring has received significant attention for an extended period @xcite @xcite @xcite @xcite . traditional methods have predominantly focused on designing heuristic or handcrafted rules to extract logical structures @xcite . for instance, text regular matching methods can be employed to differentiate headings from paragraphs. however, a notable drawback of such rule-based approaches is their specificity to certain document types, limiting their applicability to others. in recent years, the advent of deep learning has opened up new avenues for document logical structuring, with a particularly promising trend being multi-modal and multi-stage modeling @xcite . from a multi-modal perspective, the incorporation of layout and vision modalities enhances the representation of semantic structures @xcite . on the other hand, adopting a multi-stage approach involves decomposing the task into subtasks, which facilitates an easier and more manageable modeling process @xcite . while multi-modal methods excel with single-page document images, they struggle to effectively model the intricate structures of lengthy, multi-page documents. similarly, multi-stage methods encounter challenges related to error propaga-tion when concatenating all stages in real-world applications. another noteworthy direction is the transitionbased extraction @xcite . transition-based methods parse texts into structured trees from the bottom up, offering efficiency and suitability for very long documents. however, these methods focus on pairwise local context, capturing only local information while neglecting the global information of the documents. in contrast to previous works, our research introduces an end-to-end and generation-based method. this approach minimizes error propagation and enhances generalization. furthermore, our framework, incorporating global context information, helps the action generation process and efficiently predicts the logical structure of documents. ## conclusions this paper proposes seg2act, a novel method that models document logical structuring task as an end-to-end, one-pass action generation process. by leveraging the generative language model as an action generator and incorporating a global context stack, seg2act achieves significant performance and strong generalization on two benchmark datasets. for future work, we plan to explore the integration of long-context language models and multi-modal language models with the seg2act framework.