Readability-based Sentence Ranking for Evaluating Text Simplification
Sowmya Vajjala, Detmar Meurers

TL;DR
This paper introduces a readability-based pair-wise ranking method for evaluating sentence simplification, demonstrating high accuracy and analyzing linguistic features, supported by a new corpus for broader evaluation.
Contribution
It presents a novel sentence ranking approach for readability assessment and provides a new corpus enabling cross-corpus evaluation of sentence simplification.
Findings
Achieves over 80% accuracy in ranking simplified vs. unsimplified sentences
Word-level and syntactic features influence simplification degree
New corpus supports cross-corpus evaluation of sentence simplification
Abstract
We propose a new method for evaluating the readability of simplified sentences through pair-wise ranking. The validity of the method is established through in-corpus and cross-corpus evaluation experiments. The approach correctly identifies the ranking of simplified and unsimplified sentences in terms of their reading level with an accuracy of over 80%, significantly outperforming previous results. To gain qualitative insights into the nature of simplification at the sentence level, we studied the impact of specific linguistic features. We empirically confirm that both word-level and syntactic features play a role in comparing the degree of simplification of authentic data. To carry out this research, we created a new sentence-aligned corpus from professionally simplified news articles. The new corpus resource enriches the empirical basis of sentence-level simplification research, which…
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Taxonomy
TopicsText Readability and Simplification · Natural Language Processing Techniques · Topic Modeling
