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📝 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.

📜 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.

🚀 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.

🛠️ 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.

🧠 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.