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Abstract

This paper investigates the effectiveness of various text summarization models—extractive, abstractive, generative, and a proposed hybrid model—across three distinct content domains: news articles, academic papers, and social media. The study evaluates the performance of these models using standard metrics, including ROUGE, BLEU, and METEOR, providing a comprehensive assessment of each model’s strengths and limitations.

Results indicate that the extractive model excels in factual retention for news summaries, while the abstractive model demonstrates superior coherence and conciseness in academic papers. The generative model effectively captures contextual nuances in social media content, albeit with some loss in factual precision. Notably, the proposed hybrid model achieves the highest overall performance by integrating the strengths of extractive and abstractive techniques, enhancing adaptability across different domains.

These findings highlight the importance of selecting appropriate summarization strategies tailored to specific content types and demonstrate the potential of hybrid approaches in improving summary relevance and quality. Future research should explore further optimizations, including domain adaptation techniques and reinforcement learning strategies, to refine hybrid summarization methods.

Keywords

text summarization hybrid model ROUGE scores BLEU score METEOR score domain-Specific summarization

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Hybrid text summarization: Integrating extractive and abstractive models for enhanced cross-domain summarization

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