Exploring Neural Text Simplification Models

We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Text Simplification TurkCorpus NTS-SARI SARI (EASSE>=0.2.1) 37.25 # 13
BLEU 80.69 # 4