A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
Table 3: Comparison with our model and the recent neural models for text simplification. Our models achieve substantial improvement of BLEU score over baseline systems. The results are reported on the test sets.
['PWKP', 'BLEU']
We compare our model with Seq2seq-Attention and Seq2seq-Attention-w2v. Seq2seq -Attention-w2v is a Seq2seq-Attention with pretrain word embeddings. We also implement a Seq2seq-Attention model, and carefully tune it on the development set. Our implementation get 48.26 BLEU score on PWKP, and 88.97 BLEU score on EW-SEW. Our SRB outperforms all of the baseline systems, with the BLEU score of 50.18 on PWKP and the BLEU score of 89.84 on EW-SEW.