Research Paper#Natural Language Processing, Document Representation, Contrastive Learning🔬 ResearchAnalyzed: Jan 3, 2026 15:35
Skim-Aware Contrastive Learning for Long Document Representation
Published:Dec 30, 2025 17:33
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of representing long documents, a common issue in fields like law and medicine, where standard transformer models struggle. It proposes a novel self-supervised contrastive learning framework inspired by human skimming behavior. The method's strength lies in its efficiency and ability to capture document-level context by focusing on important sections and aligning them using an NLI-based contrastive objective. The results show improvements in both accuracy and efficiency, making it a valuable contribution to long document representation.
Key Takeaways
- •Proposes a novel self-supervised contrastive learning framework for long document representation.
- •Inspired by human skimming behavior, focusing on important document sections.
- •Employs an NLI-based contrastive objective for aligning relevant parts.
- •Demonstrates improvements in both accuracy and computational efficiency.
- •Applicable to legal and biomedical texts.
Reference
“Our method randomly masks a section of the document and uses a natural language inference (NLI)-based contrastive objective to align it with relevant parts while distancing it from unrelated ones.”