Open anushkasaxena07 opened 1 month ago
Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊
@abhisheks008 plz assign me this issue
Hi @anushkasaxena07 it'll be better to focus on one issue at a time.
Also in the approach part name of the proposed models/architectures are missing. Can you put some clarification on the same?
Proposed Models/Architectures: BERTSUM: A variant of BERT (Bidirectional Encoder Representations from Transformers) specifically designed for extractive summarization. T5 (Text-To-Text Transfer Transformer): A versatile model that can be fine-tuned for summarization tasks by framing them as text-to-text problems. PEGASUS: A model designed for abstractive summarization with a focus on generating high-quality summaries. Longformer: A transformer model designed to handle long documents, making it suitable for summarizing lengthy legal texts. Tools: Hugging Face Transformers, TensorFlow, PyTorch
Deep Learning Simplified Repository (Proposing new issue)
:red_circle: Project Title : Automated Legal Document Summarizer :red_circle: Aim : Create a model that can read and summarize lengthy legal documents, preserving the key legal points and clauses. :red_circle: Dataset : collected from diverse sources to ensure a variety and contents for comprehensive testing. :red_circle: Approach : Approach:
Use a pre-trained transformer model fine-tuned on a legal text dataset. Incorporate Named Entity Recognition (NER) to identify and highlight important entities (e.g., names, dates, legal terms). Evaluate the summaries for accuracy and completeness by comparing them to human-generated summaries.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎