Readability-based Sentence Ranking for Evaluating Text Simplification
This work addresses the need for better evaluation metrics in text simplification research, particularly for sentence-level analysis, though it is incremental as it builds on existing readability concepts.
The authors tackled the problem of evaluating text simplification by proposing a readability-based sentence ranking method, achieving over 80% accuracy in identifying simplified vs. unsimplified sentences, which significantly outperforms previous results.
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.