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| license: mit |
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| # Legal Document Processing Models |
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| 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. |
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| 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 |
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| 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 |
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| 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 |
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| ### Rhetorical Role Segmentation |
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| **Objective**: Segment legal text based on rhetorical roles (e.g., Facts, Arguments, Rulings). |
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| **Challenge**: Imbalanced class labels, with majority classes overshadowing minority classes. |
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| **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. |
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| #### Model Architecture |
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| This model is trained to distinguish 13 class labels in legal text, utilizing class-weighted oversampling to prevent overfitting to the majority classes. |
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| #### Data Augmentation |
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| - 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 |
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| **Objective**: Summarize legal documents by retaining essential legal entities and reducing information redundancy. |
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| **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. |
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| #### Model Architecture |
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| - 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. |
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| #### Evaluation |
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| 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 |
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| - Python 3.7 or later |
| - TensorFlow |
| - Keras |
| - Hugging Face Transformers |
| - `torch`, `rouge_score`, and other standard libraries |
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| Install the dependencies with: |
| ```bash |
| pip install -r requirements.txt |
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