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Text simplification is an aspect ofnatural language processing that involves modifying, organizing, or categorizing existing text to make it easier to understand while retaining its originalmeaning. This process is essential in today's world, where communication is increasingly complex due to advancements in science, technology, and media. Human languages are inherently intricate, with extensive vocabularies and complex structures that can be challenging for machines to handle efficiently. Researchers have found thatsemantic compression techniques can help streamline and simplify text by reducing linguistic diversity and simplifying the vocabulary used in a given context.
Text simplification involves modifying complex sentences into simpler ones to enhance readability and comprehension. Siddharthan (2006) provides an example to illustrate this process.[1] The original sentence contains multiple clauses and phrases, which can be broken down into simpler sentences for better understanding.
An approach to text simplification involveslexical simplification vialexical substitution, a process that replaces complex words with simpler synonyms. Identifying complex words is a challenge addressed by machine learning classifiers trained onlabeled data. Researchers have found that asking labelers to sort words by complexity levels yields more consistent results than the traditional method of categorizing words as simple or complex.[2]