This project explores extractive summarization—selecting the most important sentences from a document—using various different methods such as TF-IDF embeddings, sentence-level classification, Word2Vec k-means clustering, and classical ML methods. The goal is to understand how different NLP pipelines can automatically identify key information in news articles. Feel free to run any of the functions (uncomment the lines below the function) and compare their results.