Summify is a website that simplifies your lecture transcripts into an easy-to-read summary, finds sources based on keywords from the lecture, and provides review questions based on the content!
This project was bootstrapped with Create React App.
First setup flask on Mac: (instructions from here, visit for Windows and Linux)
python3 -m venv venv to create a virtual environment within the project folder
. venv/bin/activate to activate virtual environment
pip install Flask to install Flask
pip install python-dotenv to install a library required to use .flaskenv
To run the app in development mode
http://localhost:3000 to view it in the browser.
to run the flask server on port 5000
requests to the front end are proxied into port 5000
Listening to long lectures while attempting to retain information is much more challenging in an online setting. The constant screen time and easy distractions contribute to this, leading to a lack of understanding and recollection.
Summify takes a unit of text, either through a text box or through a .txt file, and returns a neat summary of the contents using bert. Along with the summary, both keywords and questions are provided to help either delve more into the particular topics or quiz yourself based on the given material!
For the front-end, we used HTML, CSS, and ReactJS. For the back-end, we used Flask and Python. For text summarization, keyword extraction, and question generation, we used existing technologies and APIs.
One of the biggest challenges that we ran into was integrating the front-end with the back-end, as none of us had experience connecting React and Flask. Most of us had also not worked with these tools before, so we learned as we worked. Figuring how to apply the technologies for text summarization into the website was also difficult, considering the model sizes and lack of time to train over an existing model.
We managed to create a working back-end and front-end with most of the members having no prior experience. We also managed to add features beyond our original plan of just text summarization, including keywords and sources with a question generator as well.
We learned a lot about natural language processing, integrating front-end and back-end, effectively using CSS, React, and Flask. Some hadn't used Flask before, and some hadn't used React before, and none of us had used them together, so we learned whichever skills were new as well as the integration of the two together.
We hope to build our own models using NLP to enable our features instead of using existing APIs/models, or at least train pre-existing models with our own data. We also hope to add a video upload option as well.