Legal Text Summarization using NLP Techniques

This project focuses on developing a natural language processing (NLP) model for summarizing legal documents. The goal is to provide concise and accurate summaries of lengthy legal texts, facilitating easier comprehension and analysis for legal professionals and individuals alike.

Features

• Extractive Summarization: Utilizes algorithms to identify and extract key sentences from legal texts.
• User-Friendly Interface: Simple command-line interface for easy interaction with the model.
• Customizable Parameters: Allows users to adjust summary length and extraction criteria based on specific needs.
• Performance Metrics: Provides metrics for evaluating the quality of generated summaries.

Technologies Used

• Python - Primary programming language.
• NLTK - Library for natural language processing tasks.
• Scikit-learn - For machine learning algorithms and model evaluation.
• Beautiful Soup - For web scraping legal documents (if applicable).
• Flask - For creating a web interface (if applicable).

Dataset

The model is trained using a dataset of legal documents, which can be sourced from publicly available legal databases or created by scraping legal texts from websites. The dataset should be formatted in a way that separates full documents and their corresponding summaries.

Installation

1. Clone the Repository

   git clone https://github.com/kzohan2000/Legal_Text_Summarization.git
   cd Legal_Text_Summarization

2. Install Required Libraries

   Create a virtual environment (optional but recommended) and install the dependencies:

   pip install -r requirements.txt

Usage

1. Run the Summarization Script

   After setting up the project, run the summarization script:

   python summarizer.py --input <path_to_legal_document> --summary_length <desired_length>

   Replace <path_to_legal_document> with the path to the legal document you wish to summarize and <desired_length> with the desired length of the summary (e.g., 2 for a two-sentence summary).

2. View Results

   The summarized output will be displayed in the console or saved to a specified output file, depending on the implementation.

Model Training

To train the summarization model, follow these steps:

1. Prepare the dataset by ensuring that it contains legal texts and their corresponding summaries.
2. Split the dataset into training and testing sets.
3. Use appropriate machine learning algorithms (e.g., TF-IDF, BERT) for extractive summarization.
4. Train the model and evaluate its performance using metrics such as ROUGE scores.

Evaluation

The performance of the summarization model can be evaluated using the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics, which compare the generated summaries to reference summaries.

Example of evaluating ROUGE scores:

from rouge import Rouge

rouge = Rouge()
scores = rouge.get_scores(generated_summary, reference_summary)
print(scores)

Future Enhancements

• Implement Abstractive Summarization techniques for more coherent and context-aware summaries.
• Enhance the model with domain-specific training to improve accuracy on specialized legal texts.
• Develop a web application for easier access and interaction with the summarization tool.
• Integrate additional evaluation metrics to provide a comprehensive assessment of summary quality.

License

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