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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: |
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1. **Rhetorical Role Segmentation** - A model designed to handle class imbalance using a class-weighted oversampling technique. |
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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) |
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2. [Model Details](#model-details) |
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- [Rhetorical Role Segmentation](#rhetorical-role-segmentation) |
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- [Summarization](#summarization) |
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3. [Requirements](#requirements) |
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4. [Setup and Installation](#setup-and-installation) |
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5. [Model Usage](#model-usage) |
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6. [Evaluation Metrics](#evaluation-metrics) |
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7. [License](#license) |
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## Project Overview |
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This project addresses key challenges in processing legal documents, specifically: |
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- **Rhetorical Role Segmentation**: Tackling class imbalance in datasets to enhance the model's ability to detect minority rhetorical roles in legal texts. |
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- **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`. |
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- 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**: |
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1. **Extractive Step**: An NER model tailored for legal documents extracts sentences with named entities, producing an initial summary. |
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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. |
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- 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 |
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- TensorFlow |
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- Keras |
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- Hugging Face Transformers |
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- `torch`, `rouge_score`, and other standard libraries |
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Install the dependencies with: |
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```bash |
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pip install -r requirements.txt |
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