Content Significance Distribution of Sub-Text Blocks in Articles and Its Application to Article-Organization Assessment

You Zhou, Jie Wang

2023

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 function 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.

Paper Citation

in Harvard Style

Zhou Y. and Wang J. (2023). Content Significance Distribution of Sub-Text Blocks in Articles and Its Application to Article-Organization Assessment. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 121-131. DOI: 10.5220/0012232600003598

in Bibtex Style

@conference{kdir23,
author={You Zhou and Jie Wang},
title={Content Significance Distribution of Sub-Text Blocks in Articles and Its Application to Article-Organization Assessment},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={121-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012232600003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Content Significance Distribution of Sub-Text Blocks in Articles and Its Application to Article-Organization Assessment
SN - 978-989-758-671-2
AU - Zhou Y.
AU - Wang J.
PY - 2023
SP - 121
EP - 131
DO - 10.5220/0012232600003598
PB - SciTePress