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.

本文通过使用 LSTM Encoder-Decoder 模型实现了句子级别的文本简化，该方法与传统的基于词法规则或手动定义规则的方法相比具有更好的效果。该模型可以学习具有一定规律的操作，如反转、排序和替换，因此可以被应用于修改句子结构、替换和删除单词等文本简化中。