  We propose a new algorithm for topic modeling, Vec2Topic, that identifies the
main topics in a corpus using semantic information captured via
high-dimensional distributed word embeddings. Our technique is unsupervised and
generates a list of topics ranked with respect to importance. We find that it
works better than existing topic modeling techniques such as Latent Dirichlet
Allocation for identifying key topics in user-generated content, such as
emails, chats, etc., where topics are diffused across the corpus. We also find
that Vec2Topic works equally well for non-user generated content, such as
papers, reports, etc., and for small corpora such as a single-document.
