There is a lot of information in the world. So much that we don’t have the time to read most of it. We made Speed Reader to cut down on the useless information and read the text that truly deserves your attention. Students, corporates, readers, it's a great resource for all.

What it does

Through extraction-based text summarisation, Speed Reader can analyze large amounts of text, remove excess and repetitive information, as well as extract important key information. With Speed Reader, students can easily summarize articles in a matter of seconds. Professors and journalists can increase productivity by using Speed Reader’s summarization tool to sort through which journals and news information they should deeply delve into. With Speed Reader, you can reduce text by up to 80% of its original size!

How we built it

We made the wireframe in Figma and then made the front-end using HTML and CSS based on the wireframe. Then, we built a web application using Flask and JavaScript. After that, we wrote a script to perform web scraping and text summarisation using nltk and beautiful soup.

Challenges we ran into

The most significant challenge we ran into was the issue of time difference. But we overcame that by enforcing a division of the labor system. We shared tasks amongst ourselves during our leisure hours and finally were able to come up with something great, Speed Reader.

Accomplishments that we're proud of

We're most proud of working together as a productive team irrespective of the differences we had. The ability to have been able to merge our talents and knowledge is what makes us feel accomplished.

What we learned

We learned how to: 1) Use the prototypes section in Figma which was what we used in animating. 2) Design and extract our wireframe into HTML & CSS right from Figma. 3) Develop extraction-based text summarisation AI. 3) Schedule times and distribute works amongst ourselves to ensure the efficient use of the time we had. 4) Clone repositories to our local drive using GitHub.

What's next for Speed Reader

We hope to: 1) Include a customizable summary option where users can decide the maximum size of text to be returned. 2) Improve on extraction of information by developing abstraction-based tech summarization.

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