https://github.com/xreedev/research-asist-tool
This project aims to simplify and summarize scientific data , convert it to a audio format as a podcast , and create a power point presentation from the paper. This helps researchers, academics and students altogether.
https://github.com/xreedev/research-asist-tool
bart beautifulsoup btech btech-project btech-projects js pptx pypdf python react requests scientific-papers summarization tf-idf vectorization word2vec
Last synced: 3 months ago
JSON representation
This project aims to simplify and summarize scientific data , convert it to a audio format as a podcast , and create a power point presentation from the paper. This helps researchers, academics and students altogether.
- Host: GitHub
- URL: https://github.com/xreedev/research-asist-tool
- Owner: xreedev
- Created: 2024-03-18T14:28:01.000Z (over 1 year ago)
- Default Branch: sree
- Last Pushed: 2024-05-22T16:28:07.000Z (over 1 year ago)
- Last Synced: 2024-05-22T16:45:03.245Z (over 1 year ago)
- Topics: bart, beautifulsoup, btech, btech-project, btech-projects, js, pptx, pypdf, python, react, requests, scientific-papers, summarization, tf-idf, vectorization, word2vec
- Language: JavaScript
- Homepage: https://research-asist-tool.vercel.app
- Size: 56.6 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
# Research Assist Tool
## Overview
Research Assist Tool simplifies and summarizes scientific data, converts it into audio podcasts, and creates PowerPoint presentations. Ideal for researchers, academics, and students.
## Features
- **Automated Text Mining**: Extracts relevant segments using the TF-IDF algorithm.
- **Content Generation**: Summarizes with BART model.
- **PowerPoint Presentation**: Creates slides with Python pptx.
- **Text-to-Speech**: Converts summaries to audio using VidLab API.
## Setup
1. **Clone Repository**:
```sh
git clone https://github.com/xreedev/Research-Asist-Tool.git
```
2. **Install Dependencies**:
```sh
pip install -r requirements.txt
```
3. **Start Backend API**:
```sh
python app.py
```
4. **Run Frontend**:
```sh
npm install
cd Frontend
npm start
```
## Usage
1. **Pre-trained Models**: Download and configure BART models.
2. **Summarization**: Provide the link through the React website.
3. **Output**: Access summaries, presentations, and audio files in the `Outputs` folder.
---
## File Structure
```
Research-Asist-Tool/
│
├── app.py # Backend API script
├── requirements.txt # Dependencies
├── README.md # Project documentation
│
├── Frontend/ # Frontend files
│ ├── src/
│ │ ├── App.js # Main React component
│ │ ├── index.js # Entry point for React
│ │ ├── components/ # React components
│ │ ├── services/ # API service functions
│ │ └── styles/ # CSS files
│ ├── public/
│ │ ├── index.html # Main HTML file
│ │ └── ...
│ └── package.json # Node.js dependencies
│
├── Models/ # Pre-trained models
│ ├── bart/ # BART models
│ ├── tf-idf/ # TF-IDF models
│ └── ...
│
├── Outputs/ # Generated outputs
│ ├── summaries/ # Text summaries
│ ├── presentations/ # PowerPoint files
│ └── audio/ # Audio files
│
└── Utils/ # Utility scripts
├── text_mining.py # Text mining functions
├── summarization.py # Summarization functions
├── ppt_creation.py # PowerPoint generation
└── text_to_speech.py # Text to speech conversion
```
## Data Flow
1. **Text Mining**:
- **Input**: Scientific paper (PDF/URL)
- **Process**: Extracts key segments using `text_mining.py`
- **Output**: Relevant text segments
2. **Summarization**:
- **Input**: Extracted text segments
- **Process**: Summarizes using BART model (`summarization.py`)
- **Output**: Summarized text
3. **PowerPoint Creation**:
- **Input**: Summarized text
- **Process**: Generates slides using `ppt_creation.py`
- **Output**: PowerPoint file
4. **Text-to-Speech**:
- **Input**: Summarized text
- **Process**: Converts to audio using `text_to_speech.py`
- **Output**: Audio file
## Contribution
- Jafar N
- Sharon T Saju
- Sreedev TS (xreedev@gmail.com)
---