2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 1 , PP: 179-192, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sini Raj Pulari 1 , Umadevi Maramreddy 2 , Shriram K. Vasudevan 3 *
Doi: https://doi.org/10.54216/FPA.200114
Artificial Intelligence's remarkable advancement and Natural Language Processing enabled innovations that fulfill various vertical requirements. News summarization has become a popular topic where systems extract valuable semantic content and generate shorter abstracts from the original content. News readers benefit from a quick understanding of essential details because an informative summary provides them with important points without forced reading of the whole article. This article covers essential NLP news summarization methods, including Abstractive summarization, Extractive summarization, and Hybrid summarization, together with recent datasets, evaluation metrics, applications and future challenges. The main benefit of this work serves both researchers by providing them with complete information about contemporary summarization developments to select suitable summarization models during application development.
Extractive summarization, Abstractive summarization, Natural Language Processing, News Recommendations
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