A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
Table 2: Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems. The results are reported on the test sets.(W: Word level; C: Character level).
['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
We compare our model with above baseline systems, including Seq2seq and Seq2seq-Attention. We refer to our proposed Semantic Relevance Based neural model as SRB. Therefore, we implement our model at the character level. Our implementation of Seq2seq-Attention has better score, mainly because we tune the hyper-parameters well on the development set. We can see SRB outperforms both Seq2seq and Seq2seq-Attention with the F-score of 33.3 ROUGE-1, 20.0 ROUGE-2 and 30.1 ROUGE-L.