From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
arXiv:2603.00021v2 Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-range dependencies and global document structure, especially for long texts. This paper proposes a data-driven method to automatically construct graph-based document representations. Building upon the recent work of Bugue\~no and de Melo (2025), we leverage the dynamic sliding-window attention module to effectively capture local and mid-range semantic dependencies between sentences, as well as structu
The increasing complexity and length of texts in AI applications necessitate more sophisticated document representations than traditional linear sequences, driving innovation in graph-based methods.
Improved document understanding through graph representations can enhance the capabilities of AI agents in classification, summarization, and other high-level NLP tasks, collapsing white-collar workflows.
AI systems gain a more nuanced understanding of document structure and long-range dependencies, moving beyond linear token sequences to more robust contextual representations.
- · NLP researchers
- · AI agent developers
- · SaaS companies leveraging NLP
- · Traditional linear NLP models
- · Companies reliant on basic NLP solutions
More accurate and efficient document processing for complex tasks like legal review or scientific literature analysis.
Acceleration of autonomous AI agent development as their comprehension and processing of information improves significantly.
Enhanced AI capabilities lead to further automation of knowledge work, impacting white-collar employment across various sectors.
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Read at arXiv cs.CL