  Text summarization and text simplification are two major ways to simplify the
text for poor readers, including children, non-native speakers, and the
functionally illiterate. Text summarization is to produce a brief summary of
the main ideas of the text, while text simplification aims to reduce the
linguistic complexity of the text and retain the original meaning. Recently,
most approaches for text summarization and text simplification are based on the
sequence-to-sequence model, which achieves much success in many text generation
tasks. However, although the generated simplified texts are similar to source
texts literally, they have low semantic relevance. In this work, our goal is to
improve semantic relevance between source texts and simplified texts for text
summarization and text simplification. We introduce a Semantic Relevance Based
neural model to encourage high semantic similarity between texts and summaries.
In our model, the source text is represented by a gated attention encoder,
while the summary representation is produced by a decoder. Besides, the
similarity score between the representations is maximized during training. Our
experiments show that the proposed model outperforms the state-of-the-art
systems on two benchmark corpus.
