• AROKIARAJ CHRISTIAN ST HUBERT
• Kiran Gowtham S
• PRABALESH P
• ROGER LOUIS

In the contemporary world, the traditional practice of reading entire newspapers daily is challenging, given the plethora of sources and potential information adulteration due to political biases. This work proposes an automated news article summarization system employing Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), to condense lengthy articles and save readers valuable time. With over 1,11,000 registered newspapers in India, the system aims to filter legitimate information from various online sources, overcoming issues such as bias and human error [7]. The proposed architecture involves fetching the top ten news articles from Google News via its API, utilizing Python's Beautiful Soup for web scraping, and employing Bidirectional Long Short-Term Memory (BiLSTM) to assess information accuracy across multiple articles. The resulting summaries provide readers with a comprehensive understanding of the news without the need to read the entire article, offering an efficient and time-saving approach to news consumption in the digital age.

Bidirectional Long Short-Term Memory (BiLSTM), employing Natural Language Processing (NLP), a subset of Artificial Intelligence (AI)

"SURVEY OF VARIOUS MACHINE LEARNING ALGORITHMS FOR TEXT SUMMARIZATION OF ONLINE NEWS ARTICLE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.f400-f407, February-2024, Available :http://www.jetir.org/papers/JETIR2402550.pdf

"SURVEY OF VARIOUS MACHINE LEARNING ALGORITHMS FOR TEXT SUMMARIZATION OF ONLINE NEWS ARTICLE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppf400-f407, February-2024, Available at : http://www.jetir.org/papers/JETIR2402550.pdf