Papers with document graphs

2 papers
Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification (2024.findings-emnlp)

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Challenge: Existing methods for document classification struggle with token limits and fail to adequately model hierarchical relationships within documents.
Approach: They propose a novel model leveraging a graph-tree structure to capture local and global dependencies.
Outcome: The proposed model captures syntactic relationships and broader document contexts without token limits and can handle arbitrarily long contexts.
Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025.findings-acl)

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Challenge: Existing document-level relation extraction models focus on individual entity pairs, limiting their ability to handle complex reasoning tasks.
Approach: They propose a document-level relation extraction framework based on global relations and entity pair reasoning that captures fine-grained interactions between entity pairs.
Outcome: The proposed framework outperforms existing models on widely-used datasets.

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