📝 Text Summarizer

A Python-based application implementing both Extractive and Abstractive text summarization techniques.
Simplify your long texts with an intuitive and user-friendly interface built using Streamlit.

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📜 Overview

The Text Summarizer application uses advanced natural language processing techniques to summarize large chunks of text into concise and meaningful content. Users can select between:

• Extractive Summarization: Extracts key sentences from the input text using the TF-IDF algorithm.
• Abstractive Summarization: Generates human-like summaries using HuggingFace’s T5 Transformer model.

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🚀 Features

• 🔄 Dual Summarization Modes:
  • Extractive: Highlights the most important sentences from the text.
  • Abstractive: Creates entirely new sentences to summarize the content.
• 💻 Streamlit-based UI: A clean, interactive interface for inputting and summarizing text.
• 🖱️ Easy-to-Use: Simply paste your text, select the summarization type, and get the summary at the click of a button.

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🛠️ Tech Stack

• Programming Language: Python 🐍
• Libraries and Tools:
  • nltk: Tokenization and stopword removal.
  • transformers: HuggingFace's T5 model for abstractive summarization.
  • streamlit: Intuitive UI for user interaction.
• Algorithms:
  • TF-IDF: For extractive summarization.
  • HuggingFace's T5-small Transformer: For abstractive summarization.

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🧠 How It Works

1. Extractive Summarization

   • Tokenizes the text and computes word frequencies, ignoring stopwords and punctuation.
   • Scores sentences based on the word frequencies.
   • Selects the top sentences to generate a summary.

2. Abstractive Summarization

   • Uses the HuggingFace T5-small Transformer model to understand and generate a concise version of the input text.
   • Produces summaries that feel natural and coherent.