Sumtext is a web-based fullstack React application that allows its users to summarize both online articles and loose text. Our application can be used by both students and adults alike. It gives them the ability to summarie articles from online sources so that they can get the gist of the article without having to read the hole thing, as well as it helps avoid clickbait. Students can use this to help summarize their notes from school, helping them write their notes. Our application uses multiple different technoliges. One of these is the server tech and database we used, Heroku and Postgres. These allowed for both remote hosting as well as integrated database connectivity. We used a React application as we think that the power of React is great for creating both web and mobile compatable applications. We also use Python to summarize the articles.


Our original idea was to create an application to check the validity of online articles. We decided that with the time constraints we decided that summarizing articles would be a good and useful app as we wanted to tackle a project that solved a issue rather than a mundane problem. By working together as a team, and countless hours we were able to get the project completed to our satisfaction


  • Summarizes online articles
  • Sumarized copied text
  • Hosted remotely
  • Fast summarization
  • Clean and crisp design
  • Clean and crisp design


There are currently no trailers available for Sumtext. Check back later for more or contact us for specific requests!


About STM

Our group is comprised of 3 members, Syed Kabir, Mark Caldeira and Tristan Roberts. These three students have a very wide range of skills that helped move the progress of the project forward. The skills in both networking and database configuration was insurmountable to the completion of the project. Also, our groups ability to adapt to different situations really helped

Sumtext Credits

Syed Kabir
Application Architect
Mark Caldeira
Server and Database Expert
Tristan Robers
Python Programmer adn NLP Integration