  Analyzing the pattern of semantic variation in long real-world texts such as
books or transcripts is interesting from the stylistic, cognitive, and
linguistic perspectives. It is also useful for applications such as text
segmentation, document summarization, and detection of semantic novelty. The
recent emergence of several vector-space methods for sentence embedding has
made such analysis feasible. However, this raises the issue of how consistent
and meaningful the semantic representations produced by various methods are in
themselves. In this paper, we compare several recent sentence embedding methods
via time-series of semantic similarity between successive sentences and
matrices of pairwise sentence similarity for multiple books of literature. In
contrast to previous work using target tasks and curated datasets to compare
sentence embedding methods, our approach provides an evaluation of the methods
'in the wild'. We find that most of the sentence embedding methods considered
do infer highly correlated patterns of semantic similarity in a given document,
but show interesting differences.
