Text Summarization Approaches for Domain-Specific Content

Abstract

Domain-specific texts—such as medical records, legal briefs, and cybersecurity reports—contain dense terminology and complex structures that challenge general-purpose summarization methods. This article surveys and advances techniques tailored to such specialized corpora. We first contextualize domain-aware summarization by contrasting extractive and abstractive paradigms and reviewing prior work, highlighting gaps in handling jargon, implicit knowledge, and heterogeneous formats. Building on these insights, we present a suite of approaches: graph-based and statistical extractive models that exploit structural cues, alongside sequence-to-sequence and transformer-based abstractive models augmented with domain adaptation strategies, including targeted fine-tuning and external knowledge integration. A detailed case study in the cybersecurity domain illustrates the end-to-end workflow—from corpus preparation to summary generation—demonstrating how domain customization boosts fidelity and coherence. Empirical evaluation using ROUGE and BLEU shows consistent gains over baseline models, while qualitative analysis pinpoints remaining challenges such as hallucination and nuance loss. We conclude by outlining research directions—richer knowledge grounding, multimodal inputs, and adaptive evaluation metrics—that promise to further close the gap between human and machine summaries in specialized fields.