BBC News Summary and Text Summarization

This project aims to demonstrate a text summarization approach using a Sequence to Sequence (Seq2Seq) model on the BBC News Summary dataset.

Dependencies

The following Python packages are required to run this script:

• os: to handle directory and file operations.
• chardet: to detect the character encoding of the text files.
• numpy: for numerical operations on arrays.
• tensorflow: for building and training the neural network models.
• keras: part of TensorFlow, used here for model definition and training.

You can install these dependencies via pip:

pip install chardet numpy tensorflow

Project Structure

• read_files(directory): This function reads all files in the specified directory, detecting and using the correct encoding for each file. It returns a list of file contents.
• load_data(main_directory): This function loads and categorizes texts and their summaries from separate directories within the main directory. It uses predefined categories (e.g., business, entertainment).

Data Loading and Preprocessing

1. Main Directory Setup: Set the main_directory to the path where the BBC News Summary data is stored.
2. Data Reading: Use load_data function to read the news articles and summaries from the directory.

Text Tokenization and Padding

• Tokenizer Setup: Initialize a Keras Tokenizer and fit it on both the texts and summaries to create a word index.
• Sequence Conversion: Convert text and summaries into sequences of integers using the tokenizer.
• Padding: Pad these sequences to ensure consistent length inputs for training the model.

Model Building: Seq2Seq Architecture

1. Encoder-Decoder Architecture: Define an LSTM-based encoder-decoder model.
2. Model Compilation: Compile the model using the Adam optimizer and sparse categorical crossentropy as the loss function.

Training

• Prepare Input and Target Data: Configure input and target data for the decoder.
• Model Training: Train the model using the prepared data.

Inference Setup

1. Encoder Model: Define a model that captures the internal states of the encoder.
2. Decoder Model: Setup the decoder to predict the next word in the sequence given the previous word and the encoder states.

Summary Generation

• decode_sequence function: For a given input sequence, this function uses the trained model to generate a text summary.
• Interactive Summarization: Allows users to enter a text and get a summary generated by the model.

Example Usage

• After training, the script provides an example of summarizing a text.
• Users can also input their own texts to get summaries in real-time.

Installation of Dependencies

Make sure to install the necessary Python packages:

pip install chardet

This project highlights the practical implementation of neural networks in processing natural language for tasks like summarization, showcasing both the challenges and solutions in handling sequence data.