Hybrid Text Summarization Using BERT-Based Extractive and T5-Based Abstractive Models

Abstract

Text summarization is essential in natural language processing due to the exponential growth of textual data. Extractive methods select salient sentences but may produce incoherent summaries, while abstractive methods generate fluent summaries but risk losing key information. This paper proposes a hybrid approach, combining BERT-based extractive summarization with T5-based abstractive summarization, capturing both informativeness and coherence. The proposed framework is evaluated on CNN/DailyMail and XSum datasets, demonstrating superior performance in ROUGE and BLEU metrics compared to individual extractive or abstractive models.

Introduction

The text presents a hybrid text summarization framework designed to address the limitations of purely extractive and purely abstractive approaches. Extractive summarization preserves factual accuracy by selecting key sentences but often lacks coherence, while abstractive summarization generates fluent, human-like summaries but may hallucinate information or omit important details. To balance informativeness and readability, the proposed approach combines both methods.

The study introduces a two-stage hybrid architecture that uses BERT for extractive sentence selection and T5 for abstractive summary generation. BERT encodes sentences and ranks them by importance, ensuring content coverage and factual fidelity, while T5 rewrites the selected sentences into a coherent and fluent summary. Detailed algorithms, workflow, and architecture are provided.

The framework is evaluated on benchmark datasets such as CNN/DailyMail and XSum, and compared against extractive-only and abstractive-only baselines. Experimental results show that the hybrid model achieves superior performance in ROUGE and BLEU metrics, demonstrating improved fluency, coherence, and informativeness while maintaining accuracy. Although the approach introduces higher computational cost and potential redundancy in long documents, it effectively overcomes key challenges in text summarization and offers a robust balance between content fidelity and readability.

Conclusion

The hybrid BERT + T5 summarization model effectively combines informativeness and fluency. Experiments demonstrate improvements over standalone extractive or abstractive models.

References

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Copyright

Copyright © 2026 Anil Kumar, Dr. V. K. Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.