  Text simplification (TS) aims to reduce the lexical and structural complexity
of a text, while still retaining the semantic meaning. Current automatic TS
techniques are limited to either lexical-level applications or manually
defining a large amount of rules. Since deep neural networks are powerful
models that have achieved excellent performance over many difficult tasks, in
this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder
model for sentence level TS, which makes minimal assumptions about word
sequence. We conduct preliminary experiments to find that the model is able to
learn operation rules such as reversing, sorting and replacing from sequence
pairs, which shows that the model may potentially discover and apply rules such
as modifying sentence structure, substituting words, and removing words for TS.
