📝 Extractive Text Summarization using TF-IDF and PageRank

This project performs Extractive Text Summarization using Natural Language Processing (NLP) techniques. It uses TF-IDF vectorization, a similarity matrix, and PageRank algorithm to select the most informative sentences from a given document. The final summary is evaluated using ROUGE scores.

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📌 Features

• Sentence tokenization using nltk
• Stopword removal
• TF-IDF vectorization with unigrams and bigrams
• Cosine similarity matrix for sentence comparison
• Graph-based ranking using networkx.pagerank()
• Extracts top-ranked sentences as summary
• Summary evaluation using ROUGE-1, ROUGE-2, and ROUGE-L

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🔐 Disclaimer

• This project implements a basic extractive text summarization approach.
• It selects the most relevant sentences from the input text based on TF-IDF and PageRank, without generating new phrases or paraphrasing like in abstractive summarization.
• It is intended for educational and learning purposes only, and not optimized for large-scale or production-level use.
• Evaluation is done using ROUGE metrics, which measure similarity to the original document but do not always reflect human judgment of quality.
• Performance may vary depending on the length and style of the input text.

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📁 File Structure

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├── ML_Project.ipynb          # Colab implementation
├── README.md                 # Project overview and documentation