Skim-Aware Contrastive Learning for Efficient Document Representation

Abstract: Although transformer-based models have shown strong performance in word- and sentence-level tasks, effectively representing long documents, especially in fields like law and medicine, remains difficult. Sparse attention mechanisms can handle longer inputs, but are resource-intensive and often fail to capture full-document context. Hierarchical transformer models offer better efficiency but do not clearly explain how they relate different sections of a document. In contrast, humans often skim texts, focusing on important sections to understand the overall message. Drawing from this human strategy, we introduce a new self-supervised contrastive learning framework that enhances long document representation. 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. This mimics how humans synthesize information, resulting in representations that are both richer and more computationally efficient. Experiments on legal and biomedical texts confirm significant gains in both accuracy and efficiency.

• The paper introduces CPE, a self-supervised contrastive learning framework that mimics human skimming to efficiently capture long document context.
• It segments documents into chunks and employs an NLI-inspired contrastive loss to strengthen intra-document relationships, achieving notable gains in macro- and micro-F1 scores.
• The method reduces computational complexity and minimizes fine-tuning needs, making it a promising solution for applications in legal and medical domains.

"Skim-Aware Contrastive Learning for Efficient Document Representation"

Introduction

The paper "Skim-Aware Contrastive Learning for Efficient Document Representation" (2512.24373) addresses the persistent challenge of generating meaningful representations for long documents, particularly in domains like legal and medical texts. Traditional transformer-based models, although effective at word- and sentence-level tasks, struggle with long documents due to computational inefficiencies and inability to fully capture document context. This work introduces a novel self-supervised contrastive learning framework—Chunk Prediction Encoder (CPE)—designed to simulate the human approach of skimming, emphasizing important text fragments to enhance document representations.

Challenges in Document Representation

Long document representation poses significant hurdles due to the complexity and resource constraints associated with existing models. Sparse attention mechanisms, while capable of handling longer inputs, often fail to capture the nuanced context of an entire document. Hierarchical transformers offer improved efficiency but lack clarity in how they relate different document sections. The paper underscores the need for document encoders that can efficiently produce high-quality embeddings without necessitating extensive fine-tuning, a pivotal requirement in domains with specialized terminology.

Methodology: Skim-Aware Contrastive Learning Framework

The proposed CPE framework innovatively harnesses a self-supervised learning approach by randomly masking document sections and utilizing a natural language inference (NLI)-based contrastive objective. This design aligns masked sections with relevant parts of the document while distancing from unrelated ones, thereby mimicking human skimming strategies:

• Hierarchical Representation: Documents are segmented into chunks, each encoded separately using pre-trained models like BERT, and aggregated using pooling strategies. This hierarchical organization effectively integrates local and global features of the document.
• Contrastive Learning Pathway: The model enhances document context capture by training encoders to distinguish between intra-document and inter-document relationships. It employs a contrastive loss that tightens the relationship between related fragments and loosens it between unrelated ones.
• NLI-inspired Contrastive Objective: By framing this learning task as an NLI problem, the model determines semantic compatibility between document fragments, thereby improving its understanding of document context.

  Figure 1: Illustration of CPE Contrastive Learning via the hierarchical transformer model.

Experimental Results

The framework's efficacy was validated through extensive experiments on datasets within the legal and biomedical domains:

• Performance Metrics: By employing datasets like ECHR, SCOTUS, and MIMIC, the CPE model demonstrated significant gains in macro-F1 and micro-F1 scores compared to both conventional and other contrastive learning models such as SimCSE and ESimCSE. These improvements highlight the model's proficiency in handling complex document encoding challenges.
• Efficiency in Representation: The method's hierarchical structure and selective chunk processing reduced computational demands while maintaining high-quality outputs, making it suitable for real-world applications in resource-constrained settings.

  Figure 2: The CPE Contrastive Learning process with the Longformer model.

Implications and Future Work

The findings from this study have profound implications for enhancing document understanding in specialized fields:

• Practical Application: By simplifying downstream task integration and minimizing the need for task-specific fine-tuning, the CPE framework can significantly expedite the deployment of NLP solutions in legal and medical contexts.
• Theoretical Insights: The alignment of CPE's methodology with human cognitive processes of information synthesis provides a novel perspective on machine learning strategies for document understanding.
• Future Research Directions: The exploration of CPE compatibility with multilingual and cross-domain datasets represents a promising avenue for extending its applicability. Moreover, advancing the underlying architecture to capture even deeper interconnections between document sections could further enrich document representations.

Conclusion

The paper presents a robust approach to long document representation with the introduction of the CPE framework. By adopting a contrastive learning paradigm inspired by human information-processing strategies, this research advances the capabilities of NLP models in handling extensive textual data in complex domains. The methodological innovations and empirical successes mark a crucial step towards more efficient and context-aware document representations, potentially paving the way for future developments in the domain.