<cite>Weighted Textual Matrix Factorization [WTMF]</cite> (<cite>Guo and Diab, 2012b</cite> ) is a latent variable model that outperforms Latent Semantic Analysis [LSA] (Deerwester et al., 1990) and Latent Dirichelet Allocation [LDA] (Blei et al., 2003) models by a large margin in the SS task, yielding state-of-the-art performance on the LI06 (Li et al., 2006 ) SS dataset. However, all of these models make harsh simplifying assumptions on how a token is generated: (1) in LSA/<cite>WTMF</cite>, a token is generated by the inner product of the word latent vector and the document latent vector; (2) in LDA, all the tokens in a document are sampled from the same document level topic distribution.