We often find ourselves struggling to read through boring articles (for school or for pleasure). We think, why can't there just be a tl;dr (too long;didn't read) summary at the end of every article that we read. We wanted to make that happen, so SherlockSummary was born.

What it does

SherlockSummary takes an article URL and uses IBM Watson's Bluemix AlchemyAPI to analyze it. The output is a combination of the article's key concepts, emotions of the authors, and keywords.

How we built it

We used Heroku for the back end and used HTML/CSS for the front-end. We implemented AlchemyAPI to scan the article given for concepts, emotions, and other criteria.

Challenges we ran into

We struggled with learning Node.js as well as implementing Bluemix's AlchemyAPI.

Accomplishments that we're proud of

We're proud of creating a web application that integrates a lot of moving parts (AlchemyAPI, Bluemix, and Heroku).

What we learned

We learned how to use Node.js (a JavaScript runtime), Heroku (a cloud application platform), and IBM Bluemix's AlchemyAPI to create a web application. This was our first adventure into APIs and intermediate web development, so this was a huge step for all of us towards understanding how to create complex web applications.

What's next for SherlockSummary

We hope to continue to clean up the user interface and add support for more of Alchemy Language's features. We might even explore using Bluemix's Language Translator to translate articles then determine their content and emotion.

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