BERT–BiLSTM based Extractive Text Summarization for Improved Information Retrieval and Efficient Knowledge Representation

Abstract

AI-based text summarization plays a critical role in managing the rapidly increasing volume of textual information by enabling efficient information retrieval and concise knowledge representation. Conventional summarization approaches often rely on heuristic rules and surface-level features, limiting their ability to capture contextual relevance and semantic coherence in long documents. To overcome these limitations, this study develops a deep learning–based extractive text summarization framework using a hybrid Bidirectional Encoder Representations from Transformers (BERT) combined with Bidirectional Long Short-Term Memory (BiLSTM) architecture. The model leverages BERT’s bidirectional transformer encoder to generate rich contextual sentence embeddings and employs a BiLSTM network to model inter-sentence dependencies and document-level structure, enabling effective identification of summary-worthy sentences while preserving original semantic content. The framework is evaluated using the BBC News Summary dataset, which contains professionally written news articles and corresponding human reference summaries. Performance is assessed through sentence-level classification metrics and ROUGE-based evaluation. Experimental results demonstrate strong effectiveness, achieving an overall accuracy of 98.5%, with balanced precision (98.7%), recall (98.2%), and F1-score (98.45%), indicating reliable sentence importance classification. In addition, the model attains competitive ROUGE ((Recall-Oriented Understudy for Gisting Evaluation) scores (ROUGE-1 = 0.56, ROUGE-2 = 0.33, and ROUGE-L = 0.52), confirming substantial overlap with human summaries. These results highlight the robustness, generalization capability, and practical applicability of the suggested BERT–BiLSTM structure for automatic text summarization and knowledge representation.