An Empirical Evaluation of Lightweight Transformer Models for Joint Entity-Relation Extraction in Cybersecurity

Joint entity-relation extraction is essential for transforming unstructured cybersecurity text into structured knowledge that supports downstream security analytics. While Transformer-based encoders have shown strong effectiveness for information extraction, real-world security applications often require models with reduced latency and memory footprint. This paper presents an empirical evaluation of lightweight Transformer models for end-To-end joint extraction in the cybersecurity domain. We employ a unified multi-Task framework that shares a Transformer encoder across entity recognition and relation classification, and optimizes both tasks jointly under a single objective. We conduct a controlled comparison of multiple compact Transformer variants using consistent training settings and evaluation criteria, and analyze the resulting accuracy-efficiency trade-offs. Our best-performing model, JointER-ModernBERT, achieves 0.9032 macro-F1 on entity recognition and 0.8549 macro-F1 on relation extraction. These findings provide practical guidance for selecting compact Transformer backbones for joint extraction in cybersecurity, and offer an evidence-based reference for deploying structured information extraction under resource-constrained settings.