  Text simplification (TS) is the process of generating easy-to-understand
sentences from a given sentence or piece of text. The aim of TS is to reduce
both the lexical (which refers to vocabulary complexity and meaning) and
syntactic (which refers to the sentence structure) complexity of a given text
or sentence without the loss of meaning or nuance. In this paper, we present
\textsc{SimpLex}, a novel simplification architecture for generating simplified
English sentences. To generate a simplified sentence, the proposed architecture
uses either word embeddings (i.e., Word2Vec) and perplexity, or sentence
transformers (i.e., BERT, RoBERTa, and GPT2) and cosine similarity. The
solution is incorporated into a user-friendly and simple-to-use software. We
evaluate our system using two metrics, i.e., SARI, and Perplexity Decrease.
Experimentally, we observe that the transformer models outperform the other
models in terms of the SARI score. However, in terms of Perplexity, the
Word-Embeddings-based models achieve the biggest decrease. Thus, the main
contributions of this paper are: (1) We propose a new Word Embedding and
Transformer based algorithm for text simplification; (2) We design
\textsc{SimpLex} -- a modular novel text simplification system -- that can
provide a baseline for further research; and (3) We perform an in-depth
analysis of our solution and compare our results with two state-of-the-art
models, i.e., LightLS [19] and NTS-w2v [44]. We also make the code publicly
available online.
