This document presents an overview of standard latent semantic indexing (LSI). It discusses how LSI uses singular value decomposition to reduce the dimensionality of a term-document matrix while retaining as much information as possible. Examples are provided to show how LSI can be used to reconstruct the original matrix and perform queries. Potential extensions like probabilistic LSI and latent Dirichlet allocation are also mentioned.