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license: mit
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# Legal Document Processing Models
This repository contains two models for processing legal documents:
1. **Rhetorical Role Segmentation** - A model designed to handle class imbalance using a class-weighted oversampling technique.
2. **Summarization** - A hybrid model that combines extractive and abstractive summarization to retain key information in legal texts.
Both models are saved in `.h5` format and can be loaded and used for inference as detailed below.
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## Table of Contents
1. [Project Overview](#project-overview)
2. [Model Details](#model-details)
- [Rhetorical Role Segmentation](#rhetorical-role-segmentation)
- [Summarization](#summarization)
3. [Requirements](#requirements)
4. [Setup and Installation](#setup-and-installation)
5. [Model Usage](#model-usage)
6. [Evaluation Metrics](#evaluation-metrics)
7. [License](#license)
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## Project Overview
This project addresses key challenges in processing legal documents, specifically:
- **Rhetorical Role Segmentation**: Tackling class imbalance in datasets to enhance the model's ability to detect minority rhetorical roles in legal texts.
- **Summarization**: Generating high-quality summaries using a hybrid extractive-abstractive approach to capture crucial legal entities and condense information.
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## Model Details
### Rhetorical Role Segmentation
**Objective**: Segment legal text based on rhetorical roles (e.g., Facts, Arguments, Rulings).
**Challenge**: Imbalanced class labels, with majority classes overshadowing minority classes.
**Solution**: A class-weighted oversampling method that calculates an oversampling rate based on the frequency distribution of class labels, ensuring adequate representation of minority classes.
#### Model Architecture
This model is trained to distinguish 13 class labels in legal text, utilizing class-weighted oversampling to prevent overfitting to the majority classes.
#### Data Augmentation
- A normalized frequency distribution of class labels was created, as visualized in `Figure 5: Unbalanced Dataset`.
- Using this distribution, we applied a class-weighted oversampling rate to increase minority class samples proportionally.
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### Summarization
**Objective**: Summarize legal documents by retaining essential legal entities and reducing information redundancy.
**Approach**:
1. **Extractive Step**: An NER model tailored for legal documents extracts sentences with named entities, producing an initial summary.
2. **Abstractive Step**: A fine-tuned Longformer Encoder-Decoder (LED) model refines the extractive summary to create a concise output.
#### Model Architecture
- The extractive component utilizes a legal-specific Named Entity Recognition (NER) model.
- The abstractive summarization is achieved using a pre-trained LED model on Hugging Face, further fine-tuned on legal data.
#### Evaluation
Each component was evaluated using ROUGE metrics, with the hybrid approach demonstrating superior performance in terms of informativeness and conciseness.
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## Requirements
- Python 3.7 or later
- TensorFlow
- Keras
- Hugging Face Transformers
- `torch`, `rouge_score`, and other standard libraries
Install the dependencies with:
```bash
pip install -r requirements.txt
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