First, <cite>Choi et al.'s</cite> segmentation procedure does not rely on supervised learning in which a system learns how to efficiently segment a text from training data. The LSA corpus only intervenes in an indirect manner by allowing the extraction of semantic proximities between words that are then used to compute similarities between parts of the text to segment (see Section 2 for details). Second, <cite>Choi et al.</cite> employed a large number of small test samples to evaluate their algorithm, each making up-on average-0.15% of the LSA corpus. The present study shows, however, that the presence of the test materials in the LSA corpus has an important effect, but also that the generic semantic knowledge derived from large corpora clearly improves the segmentation accuracy.