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With the increasing interest in natural language processing, text summarization has become essential for condensing large volumes of data into concise and meaningful summaries. Extractive summarization, which involves selecting key sentences based on textual features, has gained attention due to its efficiency and effectiveness. This research explores extractive summarization using multiple machine learning classifiers, including Support Vector Machines (SVM), Logistic Regression (LR), Decision Trees (DT), K-Nearest Neighbors (KNN), and Random Forest (RF). Our findings indicate that the Random Forest model achieved the highest accuracy, reaching 80% in classifying sentences for summary generation. Additionally, we evaluated text classification on the same BBC dataset using ChatGPT, which attained an accuracy of 62%. Furthermore, comparisons with results from prior research confirm the competitive performance of our approach, reinforcing the potential of machine learning models in extractive summarization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Journal Abbreviation: Lect. Notes Networks Syst.