Task-01_Text-Summarizer

Text-Summarizer

This project demonstrates a simple implementation of extractive text summarization using Python and the Natural Language Toolkit (NLTK) library. The approach is unsupervised, meaning it doesn’t require pre-training a model. Instead, it scores sentences based on word frequencies and selects the most important ones.

Features

• Extractive summarization using word frequency
• Tokenization of text into words and sentences
• Removal of common English stopwords
• Scoring of sentences based on term frequency
• Generation of a concise summary from the input text

Installation

1.Install required libraries:

 pip install nltk 

2.Download necessary NLTK data:

 import nltk  
nltk.download('punkt')  
nltk.download('stopwords')