A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
Table 5: Results of our model and state-of-the-art systems. SRB achieves the best BLEU scores compared with the related systems on PWKP and EW-SEW. The results are reported on the test sets.
['PWKP', 'BLEU']
We also compare SRB with other models for text simplification, which are not limit to neural models. On PWKP dataset, we compare SRB with NTS, NTS-w2v, DRESS and DRESS-LS. We run the public release code of NTS and NTS-w2v provided by \nameciteNisioiEA2017, and get the BLEU score of 47.52 and 48.10 respectively. As for DRESS and DRESS-LS, we use the scores reported by \nameciteZhangEA2017. The goal of DRESS is not to generate the outputs closer to the references, so BLEU of DRESS and DRESS-LS are relatively lower than NTS and NTS-w2v. SRB achieves a BLEU score of 50.18, outperforming all of the previous systems. On EW-SEW dataset, we compare WEAN-dot with PBMT-R, SBMT-SARI, and the neural models described above. We do not find any public release code of PBMT-R and SBMT-SARI. Fortunately, \nameciteXuEA2016 provides the predictions of PBMT-R and SBMT-SARI on EW-SEW test set, so that we can compare our model with these systems. It shows that the neural models have better performance in BLEU, and WEAN-dot achieves the best BLEU score with 89.84.