I created Tuchu because I wanted to increase my reading efficiency. It is a tool that automatically highlights the important parts of any document. Most documents take about two to three seconds to process. It's directed at students, researchers, or anyone with a reading list and little time for that matter.
During my studies I had to go through a lot of literature, for example when I had to select relevant material for my thesis, or when I had to familiarize myself with a course's reading list. Tuchu helped me to get up to speed in these cases. What started off as a command-line Python script is now a web application that does its analysis without any back-end. I don't get to see your documents.
The underlying algorithm that selects what's relevant is called TextRank, an unsupervised summarization method [1]. It models a document (or a collection thereof) as a fully connected graph. Its nodes are parts of the text — I use sentences — and the edges between them are weighted by a similarity measure, in my case simple word overlap. The subset of sentences with the highest PageRank are then highlighted. For good measure, I also highlight sentences that contain signal words that — in my academic experience — signify importance.
It's important to note that Tuchu is not a substitute for doing your own reading. It could make you a faster reader by directing your attention to the important parts, but you'll still have to ponder about the true essence of a document yourself.