# Efficient Extractive Text Summarization for Online News Articles Using Machine Learning

arXiv: https://arxiv.org/abs/2509.15614 · HTML: https://mlsanity.com/paper/2509.15614/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Neural Latent Extractive Document Summarization](https://mlsanity.com/paper/1808.07187/) (2018)
- [Adversarial Attacks on Large Language Models Using Regularized Relaxation](https://mlsanity.com/paper/2410.19160/) (2024)
- [Classify or Select: Neural Architectures for Extractive Document Summarization](https://mlsanity.com/paper/1611.04244/) (2016)
- [Adversarial Attack on Large Language Models using Exponentiated Gradient Descent](https://mlsanity.com/paper/2505.09820/) (2025)
- [Automatic News Summerization](https://mlsanity.com/paper/2310.11520/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Hybrid Hierarchical 1D-CNN-BiLSTM Framework for Extractive Summarization of Biomedical and Clinical Text](https://mlsanity.com/paper/2609.13481/) (2026)

## Abstract (arXiv)

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.