Text Summarization Project ✍️📖

A deep learning-based project for summarizing articles into concise highlights. This project uses natural language processing (NLP) and attention mechanisms to extract meaningful summaries from long text articles.

---

✨ Features

• 📄 Clean and preprocess text data for NLP tasks.
• 🖼 Visualize data insights with histograms and word clouds.
• 🔠 Tokenize and pad sequences for training deep learning models.
• 🧠 Build and train a sequence-to-sequence model with attention for text summarization.
• 💾 Save trained models and tokenizers for future use.

---

📚 Technologies Used

• Python: The core programming language.
• Libraries:
  • 🔹 pandas, numpy: For data processing and numerical computations.
  • 🔹 matplotlib, seaborn, wordcloud: For data visualization.
  • 🔹 tensorflow.keras: For building and training the deep learning model.
  • 🔹 sklearn: For splitting datasets into training and testing sets.

---

🔄 How to Run

1. Clone the repository and navigate to the project directory:

   git clone <repository_url>
   cd text_summarization_project

2. Install the required libraries:

   pip install pandas numpy matplotlib seaborn wordcloud tensorflow scikit-learn

3. Place your dataset (train.csv) in the root directory. Ensure it has the required columns:
   • article
   • highlights
4. Run the main script:

   python main.py

---

🧪 Workflow

1. Preprocessing:
   • Clean and standardize text by removing special characters, numbers, and extra spaces.
   • Tokenize and pad the sequences for both articles and highlights.
2. Visualization:
   • Generate histograms for article and summary lengths.
   • Create word clouds to highlight frequently used terms.
3. Model Training:
   • Use LSTM and attention mechanisms to develop a seq2seq model for summarization.
   • Optimize model performance with callbacks like EarlyStopping and ReduceLROnPlateau.
4. Saving Artifacts:
   • Save the trained model and tokenizers for future inference.

---

🔜 Future Improvements

• ➕ Add support for multilingual text summarization.
• 🚀 Enhance model architecture for better performance.
• 📊 Implement an interactive dashboard for summary generation.

---

🖼 Screenshots

---

🔒 License

This project is licensed under the MIT License. See the LICENSE file for details.