From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
arXiv:2603.00021v1 Announce Type: new Abstract: 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 structural relations within documents. Graph Attention Networks (GATs) trained on our learned graphs achieve competitive results on document classification while requiring lower computational resources than previous approaches. We further present an exploratory evaluation of the proposed graph construction method for extractive document summarization, highlighting both its potential and current limitations. The implementation of this project can be found on GitHub.
arXiv:2603.00021v1 Announce Type: new Abstract: 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 structural relations within documents. Graph Attention Networks (GATs) trained on our learned graphs achieve competitive results on document classification while requiring lower computational resources than previous approaches. We further present an exploratory evaluation of the proposed graph construction method for extractive document summarization, highlighting both its potential and current limitations. The implementation of this project can be found on GitHub.
Executive Summary
The paper introduces a novel data-driven framework for constructing context-aware graph representations of documents, extending prior work by integrating a dynamic sliding-window attention module to capture both local semantic dependencies and structural document relations. The authors employ Graph Attention Networks (GATs) trained on these learned graphs to achieve competitive performance in document classification with reduced computational overhead. An exploratory evaluation for extractive summarization is also presented, indicating promising avenues but also acknowledging current constraints. The work is open-source, enhancing reproducibility and accessibility. Overall, the contribution bridges a gap between global and local semantic modeling in document analysis.
Key Points
- ▸ Dynamic sliding-window attention module integrated for local/mid-range semantic dependencies
- ▸ Graph Attention Networks (GATs) trained on learned graphs yield competitive classification results with lower computational cost
- ▸ Exploratory evaluation for extractive summarization highlights both potential and limitations
Merits
Innovation
The integration of a dynamic sliding-window attention module represents a significant step forward in contextual graph modeling, offering a more nuanced representation of document structure.
Demerits
Scope Limitation
While the exploratory evaluation for summarization is commendable, the current evaluation is exploratory and lacks depth or comparative benchmarks, limiting the generalizability of findings in this domain.
Expert Commentary
This work represents a thoughtful evolution of graph-based representation techniques in NLP. The use of dynamic attention mechanisms to model both local and structural dependencies is particularly compelling, as it addresses a longstanding challenge in capturing contextual coherence without sacrificing scalability. The authors wisely acknowledge the exploratory nature of their summarization evaluation, which demonstrates methodological humility and transparency. While the current results are promising, future work should aim to integrate more rigorous comparative analyses with established summarization frameworks (e.g., BERTSUM, PEGASUS) to better quantify the relative advantages of their graph-based approach. Moreover, extending the analysis to multi-document or cross-domain scenarios could unlock broader applicability. The paper’s open-source commitment is a model for reproducibility in computational linguistics and should be emulated in future research.
Recommendations
- ✓ 1. Extend the summarization evaluation with benchmarked comparisons against state-of-the-art extractive models.
- ✓ 2. Explore the applicability of the graph construction method to multi-document or cross-domain classification tasks to assess scalability and generalizability.