An experiment in computationally writing and compiling academic papers based around machine learning. 

Currently working on: 
- Automated scraping of academic articles

Completed:

- Initial user interface (web based) and initial hacky backed 
- Set up Git repo (10/13/11)

- 

Flow:
1) User enters a topic string. Ex) Canadian macro economic policy (Possibly add subtopics to refine search)
2) InstantPaper references online journal and academic sources and delivers a rating (0-100) on the availability of information on the chosen topic
3) Using a PageRank-esq algorithm, create a hierachy of academic papers based on imporance. Scrape content from top 100 papers on topics and using machine learning find commonly cited argument
4) Extrapelate 3-4 top 'themes' for each topic. These will act as individual body paragraphs
5) Rank indexed papers context and relevence to themes and add 5-6 'points' to each theme
6) Intelligently gather author information so we can get sentences like: "Scottish political economist Adam Smith wrote in the  'Wealth of Nations' that "arument/quote..." (Could also cross reference this information w/ Wikipedia)
7) Allow the user to select reference and citation format
8) Have a few other user settings: max word count, limit citations to 'n' length, user name, class, professor...etc.
9) Output as PDF/DOC/LaTeX