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Related Experiment Videos

Box-enhanced context fusion for citation intent classification.

Jinwen Yang1, Zhijuan Du1

  • 1Inner Mongolia University, Hohhot, 010021, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces GLB-Cite, a new model for citation intent classification that improves accuracy by considering citation marker position and text hierarchy. GLB-Cite achieves state-of-the-art results on benchmark datasets.

Keywords:
Citation intent classificationCitation marker positionContext fusionGeometry-awareGlobal context representationJoint optimization

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Area of Science:

  • Information Science
  • Scientometrics
  • Natural Language Processing

Background:

  • Citation intent classification is crucial for understanding scholarly communication.
  • Current methods often overlook citation marker position and text hierarchies.
  • This can lead to suboptimal performance in categorizing citation behaviors.

Purpose of the Study:

  • To propose GLB-Cite, a novel model for citation intent classification.
  • To address limitations of existing text-encoding approaches.
  • To improve the modeling of citation contexts and authorial intent.

Main Methods:

  • GLB-Cite fuses local citation marker information with global context.
  • It uses parallel vector and box embedding spaces for semantic and geometric modeling.
  • The model jointly optimizes semantic expressiveness and boundary modeling.

Main Results:

  • GLB-Cite achieves state-of-the-art performance on SciCite and ACL-ARC datasets.
  • Achieved macro-F1 scores of 87.69% on SciCite and 79.79% on ACL-ARC.
  • Demonstrates significant gains, particularly for classes with overlapping semantics.

Conclusions:

  • GLB-Cite offers a more effective approach to citation intent classification.
  • The model's fused representation balances semantic and geometric properties.
  • This advancement aids in better understanding citation roles in scientific literature.