Authors:
You Zhou
and
Jie Wang
Affiliation:
Richard Miner School of Computer & Information Sciences, University of Massachusetts, Lowell, MA 01854, U.S.A.
Keyword(s):
Content Significance Distribution, Embedding Similarity, Article Structure, Beta Distribution, Article-Organization Assessment.
Abstract:
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 fun
ction of the beta distribution with certain values of α and β 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.
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