Literature review can be a long process. In order to make it easier, we wanted to build a service that finds the seminal papers in any field of study.

What it does

Main page

Main page

Raw output

Raw output

what results should look like

What results should look like

It uses an NLP algorithm called Latent Dirichlet Allocation to generate a list of topics given a corpus of the paper abstract and title. It finds a recursive implementation to find papers with the highest numbers of citations, which indicates a higher quality.

How We built it

plan plan2

Using Algorithmia to creates relevant topics to the search paper and queried Google Scholar, which returns a list of relevant papers. We plan to recursively generate topics in order to find more frequently cited papers. After recursing enough times, we will take the most highly-cited papers and display them to the user.

Challenges We ran into

none of us know server side programming None of us knew anything about server-side programming. We spent a lot of time configuring our server and learning Flask.

Accomplishments that We're proud of

We put together a working product with very little prior experience in web programming.

What We learned

We learned a lot of new technologies, APIs, and improvising on the go.

What's next for intuition

Replace humanity.

Also, fine-tune the algorithm for better accuracy and user experience.

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