  We explore how to capture the significance of a sub-text block in an article
and how it may be used for text mining tasks. A sub-text block is a
sub-sequence of sentences in the article. We formulate the notion of content
significance distribution (CSD) of sub-text blocks, referred to as CSD of the
first kind and denoted by CSD-1. In particular, we leverage Hugging Face's
SentenceTransformer to generate contextual sentence embeddings, and use
MoverScore over text embeddings to measure how similar a sub-text block is to
the entire text. To overcome the exponential blowup on the number of sub-text
blocks, we present an approximation algorithm and show that the approximated
CSD-1 is almost identical to the exact CSD-1. Under this approximation, we show
that the average and median CSD-1's for news, scholarly research, argument, and
narrative articles share the same pattern. We also show that under a certain
linear transformation, the complement of the cumulative distribution function
of the beta distribution with certain values of $\alpha$ and $\beta$ resembles
a CSD-1 curve. We then use CSD-1's to extract linguistic features to train an
SVC classifier for assessing how well an article is organized. Through
experiments, we show that this method achieves high accuracy for assessing
student essays. Moreover, we study CSD of sentence locations, referred to as
CSD of the second kind and denoted by CSD-2, and show that average CSD-2's for
different types of articles possess distinctive patterns, which either conform
common perceptions of article structures or provide rectification with minor
deviation.
