Enhancing News Article Summarization with Machine Learning

Anand Prakash

doi:10.55938/ijgasr.v3i4.152

Authors

Anand Prakash Indiana University

DOI:

https://doi.org/10.55938/ijgasr.v3i4.152

Keywords:

News Summarization, Machine Learning, Python, Automated Summarization, Feature Extraction

Abstract

The exponential growth of online news content has created a pressing need for automated summarization tools to help process and condense information effectively. This paper presents a machine learning-based approach to summarizing news articles, focusing on techniques that produce concise and coherent summaries. The methodology includes text preprocessing steps such as tokenization, stop-word removal, and stemming, followed by feature extraction and model training using machine learning frameworks. Libraries such as NLTK and TensorFlow are employed to facilitate text processing and the implementation of the summarization model. The proposed approach is evaluated against baseline models, showcasing its ability to generate high-quality summaries efficiently. The research highlights the advantages of machine learning in automating news summarization, saving time and effort for readers and editors. Challenges such as handling nuanced language and context are discussed, and the paper outlines future research directions to address these limitations and further enhance summarization performance. This study contributes to the growing field of automated news summarization by providing a practical, scalable, and effective solution. It underscores the potential of machine learning to revolutionize how news content is consumed and processed, offering valuable insights for advancing this domain.

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