  The processing of legal texts has been developing as an emerging field in
natural language processing (NLP). Legal texts contain unique jargon and
complex linguistic attributes in vocabulary, semantics, syntax, and morphology.
Therefore, the development of text simplification (TS) methods specific to the
legal domain is of paramount importance for facilitating comprehension of legal
text by ordinary people and providing inputs to high-level models for
mainstream legal NLP applications. While a recent study proposed a rule-based
TS method for legal text, learning-based TS in the legal domain has not been
considered previously. Here we introduce an unsupervised simplification method
for legal texts (USLT). USLT performs domain-specific TS by replacing complex
words and splitting long sentences. To this end, USLT detects complex words in
a sentence, generates candidates via a masked-transformer model, and selects a
candidate for substitution based on a rank score. Afterward, USLT recursively
decomposes long sentences into a hierarchy of shorter core and context
sentences while preserving semantic meaning. We demonstrate that USLT
outperforms state-of-the-art domain-general TS methods in text simplicity while
keeping the semantics intact.
