Fine-Tuned Language Models for Domain-Specific Summarization and Tagging

2025-10-29

AI Summary

Rapid evolution of subcultural language poses significant challenges for understanding unstructured text, particularly in low-resource, high-dynamics domains such as political and security monitoring. Method: This paper proposes a cross-lingual information extraction framework integrating instruction-tuned large language models (LLMs) with named entity recognition (NER). It employs domain-adapted cross-lingual transfer to perform Chinese political and security domain instruction tuning on the English-based LLaMA model. Contribution/Results: The resulting model achieves superior performance over specialized Chinese models in both abstractive summarization and fine-grained label annotation on emerging Chinese web corpora. Quantitative evaluation using BLEU and ROUGE metrics demonstrates substantial improvements in summary quality and NER accuracy. The framework enables real-time document classification and structured knowledge extraction. This work delivers a scalable, adaptive NLP solution for security surveillance and knowledge management in rapidly evolving, resource-constrained domains.

Technology Category

• Natural Language Processing: Information Extraction
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Security

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a pipeline integrating fine-tuned large language models (LLMs) with named entity recognition (NER) for efficient domain-specific text summarization and tagging. The authors address the challenge posed by rapidly evolving sub-cultural languages and slang, which complicate automated information extraction and law enforcement monitoring. By leveraging the LLaMA Factory framework, the study fine-tunes LLMs on both generalpurpose and custom domain-specific datasets, particularly in the political and security domains. The models are evaluated using BLEU and ROUGE metrics, demonstrating that instruction fine-tuning significantly enhances summarization and tagging accuracy, especially for specialized corpora. Notably, the LLaMA3-8B-Instruct model, despite its initial limitations in Chinese comprehension, outperforms its Chinese-trained counterpart after domainspecific fine-tuning, suggesting that underlying reasoning capabilities can transfer across languages. The pipeline enables concise summaries and structured entity tagging, facilitating rapid document categorization and distribution. This approach proves scalable and adaptable for real-time applications, supporting efficient information management and the ongoing need to capture emerging language trends. The integration of LLMs and NER offers a robust solution for transforming unstructured text into actionable insights, crucial for modern knowledge management and security operations.

Problem

• Addressing domain-specific text summarization and tagging challenges
• Overcoming sub-cultural language barriers in automated information extraction
• Enhancing cross-lingual reasoning for specialized content processing

Innovation

• Fine-tuned LLMs for domain-specific summarization and tagging
• Integrated NER for structured entity extraction from text
• Instruction fine-tuning boosts accuracy on specialized corpora