  Document similarity is an important part of Natural Language Processing and
is most commonly used for plagiarism-detection and text summarization. Thus,
finding the overall most effective document similarity algorithm could have a
major positive impact on the field of Natural Language Processing. This report
sets out to examine the numerous document similarity algorithms, and determine
which ones are the most useful. It addresses the most effective document
similarity algorithm by categorizing them into 3 types of document similarity
algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in
our work using a series of benchmark datasets and evaluations that test every
possible area that each algorithm could be used in.
