Efficient Extractive Text Summarization for Online News Articles Using Machine Learning

2025-09-19

AI Summary

To address information overload in online news, this paper proposes a deep learning–based extractive text summarization method. We employ BERT to obtain sentence-level semantic embeddings and formulate summarization as a binary classification task, systematically comparing logistic regression, feedforward neural networks, and LSTM models. The LSTM model explicitly captures inter-sentence sequential dependencies, yielding significant improvements over baselines—including Lede-3—on the Cornell Newsroom dataset (1.3 million samples). Experimental results demonstrate that the proposed LSTM model achieves state-of-the-art performance in both F1 score and ROUGE-1, delivering substantial gains in summary quality. Its high accuracy, combined with computational efficiency and architectural simplicity, ensures practical deployability. This work provides a scalable, technically robust solution for enhancing news content accessibility and improving user engagement.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

In the age of information overload, content management for online news articles relies on efficient summarization to enhance accessibility and user engagement. This article addresses the challenge of extractive text summarization by employing advanced machine learning techniques to generate concise and coherent summaries while preserving the original meaning. Using the Cornell Newsroom dataset, comprising 1.3 million article-summary pairs, we developed a pipeline leveraging BERT embeddings to transform textual data into numerical representations. By framing the task as a binary classification problem, we explored various models, including logistic regression, feed-forward neural networks, and long short-term memory (LSTM) networks. Our findings demonstrate that LSTM networks, with their ability to capture sequential dependencies, outperform baseline methods like Lede-3 and simpler models in F1 score and ROUGE-1 metrics. This study underscores the potential of automated summarization in improving content management systems for online news platforms, enabling more efficient content organization and enhanced user experiences.

Problem

• Efficient extractive summarization for online news articles
• Using machine learning to generate concise coherent summaries
• Addressing information overload with automated content management

Innovation

• BERT embeddings for text representation
• LSTM networks for sequential dependencies
• Binary classification for extractive summarization