A Review of the Most Important Studies on Automated Text Simplification Evaluation Metrics

Abstract

Text Simplification is described as the process of transforming natural language text, both lexical and syntactic. The structure and grammar of the output must be considerably simplified, and understandability and readability should be improved, while original meaning and information are maintained. Text simplification is a fast-growing domain and can be used for several applications, such as preprocessing tool in the pipeline of natural language processing tasks as well as helping people with special needs. While the automated text simplification operation itself is challenging, the bench-marking and evaluating of this task is even more elaborate and controversial. Although there have been some reviews in the context of text simplification, no specific survey has been done on the question of evaluation metrics and methods of text simplification. In this paper, we review the most significant studies identified out of more than 300 studies of the last three decades in the field of text simplification, focusing on evaluation metrics, methods and corpora. There are different evaluation metrics, methods and corpora for text simplification based on approaches, datasets, and algorithms used to perform simplification task. We made a review of these different metrics and provided results of high-quality research studies on each criterion.