Rosamma, K.S.Patil, N.2026-02-032025SN Computer Science, 2025, 6, 6, pp. -2662995Xhttps://doi.org/10.1007/s42979-025-04202-yhttps://idr.nitk.ac.in/handle/123456789/20152With the rapid expansion of digital content, efficient and accurate text summarization methods are essential to condense information effectively. Traditional extractive summarization approaches often fail to capture the most relevant sentences because they rely on simple heuristics. This research introduces a more advanced summarization model that integrates lead scoring with a Bidirectional Gated Recurrent Unit (BiGRU) network. The proposed Hybrid Lead Scoring BiGRU Model leverages the initial relevance of lead sentences while enhancing context comprehension through the BiGRU architecture. Despite advancements in text summarization, existing methods struggle to maintain contextual coherence and accurately identify key sentences. To address these challenges, our model combines the strengths of lead scoring, which selects the first five sentences of an article, with deep learning techniques. The BiGRU then processes these selections bi-directionally to capture dependencies from both past and future contexts, ultimately selecting the top three sentences for the summary. The model was evaluated using the CNN/Daily Mail dataset and showed promising results. During training, the model achieved the best validation loss of 0.4442, with an early stopping mechanism preventing overfitting. The test phase yielded a test loss of 0.4299, demonstrating good generalization performance. Additionally, selected generated summaries produced showed better performance with ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.7143, 0.6000, and 0.5714, respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.BiGRUExtractive summarizationLead scoringROUGE scoresText summarization