Readability-based Sentence Ranking for Evaluating Text Simplification

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 so far relied on a single resource. Most importantly, it facilitates cross-corpus evaluation for simplification, a key step towards generalizable results.