arXiv Summarizer: Project Abstractify

This is a Flask app that uses a fine-tuned BART model (trained on arXiv datasets) to summarize research papers. The goal was to create a simple, easy-to-use tool for researchers to get concise summaries of lengthy papers.

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Project Status

The project is completed! Here's what it includes:

1. A functional Flask app to upload research papers and generate summaries.
2. A fallback mechanism that formats outputs via a T5 base model for improved readability.

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🛠️ Setup Instructions

Requirements

• Python 3.8 or above.
• Virtual environment tools like conda or venv are highly recommended.

Steps to Run the Project

1. Clone the repository:

   git clone https://github.com/Firojpaudel/arXiv_Summarizer.git
   cd arXiv_Summarizer

2. Set up a virtual environment (recommended):

   Using conda:

   conda create -n arxiv_summarizer python=3.10  
   conda activate arxiv_summarizer  

   Or using venv:

   python -m venv venv  
   source venv/bin/activate (Linux/Mac)  
   venv\Scripts\activate (Windows)

3. Install dependencies:

   pip install -r requirements.txt

4. Download the fine_tuned_bart model:

   The model is not included here due to its size. Please download it from the following link and place it inside the directory:

   Download Model

5. Run the app:

   python app.py

   The app will be hosted locally at http://127.0.0.1:5000/

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🖼️ How It Looks

Here’s a quick preview of the app in action:

🏠 Homepage:

This is where it all starts! Upload your research paper and get started.

📝 Summarization in Action:

Once your paper is uploaded, the app gets to work, breaking down complex research into digestible summaries.

📄 Final Summary Output

A clear, concise summary is generated for your research paper, formatted beautifully in markdown for readability.

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🧠 Learnings & Challenges

• While BART works well for summarization, certain outputs lacked consistency due to variations in paper formats.
• The project demonstrated the limitations of fine-tuning on older datasets with diverse formatting styles.
• Future iterations could use a hybrid approach or train on more specialized datasets for better results.

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📂 Repository Structure

arXiv_Summarizer/  
│  
├── app.py                   # Main Flask app  
├── fine_tuned_bart/         # Directory to store the fine_tuned_bart model (needed to be downloaded from drive) 
├── templates/               # HTML templates for the app  
├── static/                  # Static files (CSS, JS, images)  
├── README_images/           # Directory for README images  
├── requirements.txt         # Dependencies  
└── README.md                # Project documentation (this file)    

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🤝 Contributions & License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it as per the license terms.

How You Can Contribute

We welcome all contributors who want to add value to this project! Whether it's improving summarization quality, refining the interface, or optimizing performance, your contribution matters.

To contribute, follow these steps:

1. Fork the repository.

2. Create your feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Added a cool new feature"

4. Push to the branch:

   git push origin feature-name

5. Open a pull request.

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