The Summarization of News Articles using Extractive Techniques involves a systematic approach to distill essential information from a given news article. This text summarization approach for news articles involves a multi-step process. Initially, preprocessing techniques are applied to clean and structure the text, encompassing tasks such as removing HTML tags, converting text to lowercase, tokenization, and stopword removal. A Word2Vec model is then trained on the preprocessed text, enabling the conversion of words into semantically meaningful embeddings. The core of the summarization process employs the TextRank algorithm, utilizing sentence embeddings derived from the Word2Vec model to construct a similarity matrix and represent sentences in a graph. The ranking produced by TextRank determines the most important sentences, forming the extractive summary. Postprocessing steps, including filtering based on sentence length and redundancy removal, enhance the coherence and readability of the final summary. This comprehensive approach facilitates the extraction of key information from news articles, providing concise and informative summaries.