AI Summarization Model

This repository contains an AI model for generating summaries of news articles. The model has been trained on the Alpaca dataset, a variant specifically designed for the CNN/Daily Mail summarization task.

Dataset

The Alpaca dataset used for training this model contains news articles sourced from CNN and Daily Mail, along with corresponding human-generated summaries. This dataset is commonly used in the field of natural language processing for tasks related to text summarization. You can find the dataset here.

Model Description

The AI summarization model utilizes natural language processing techniques to analyze and summarize news articles. It tokenizes the input text, removes stopwords and punctuation, and lemmatizes the tokens to prepare them for summarization. The model then builds a frequency distribution of words in the input documents and selects the most common words to generate the summary.

Usage

To use the model for summarization, follow these steps:

1. Clone the repository:

   git clone https://github.com/your_username/summarization-model.git
   cd summarization-model

2. Install the required dependencies:

   pip install -r requirements.txt

3. Load the trained model:

   import pickle
   
   with open('summary_model.pkl', 'rb') as f:
       summary_model = pickle.load(f)

4. Provide input text to generate summaries:

   # Example input text
   input_text = "The new smartphone features a stunning design with a sleek metal body and a vibrant AMOLED display."
   
   # Generate summary
   summary = generate_summary(input_text, summary_model)
   print("Generated Summary:", summary)

Contribution

Contributions to improve the model's performance or extend its capabilities are welcome. If you encounter any issues or have suggestions for enhancements, feel free to open an issue or submit a pull request.

License

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