Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
Table 1: Comparison with state-of-the-art models on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based.
['[BOLD] Models', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L']
We first compare our model with Seq2Seq baseline. It shows that our superAE model has a large improvement over the Seq2Seq baseline by 7.1 ROUGE-1, 6.1 ROUGE-2, and 7.0 ROUGE-L, which demonstrates the efficiency of our model. Moreover, we compare our model with the recent summarization systems, which have been evaluated on the same training set and the test sets as ours. Their results are directly reported in the referred articles. It shows that our superAE outperforms all of these models, with a relative gain of 2.2 ROUGE-1, 1.8 ROUGE-2, and 2.0 ROUGE-L. We also perform ablation study by removing the adversarial learning component, in order to show its contribution. It shows that the adversarial learning improves the performance of 1.5 ROUGE-1, 0.7 ROUGE-2, and 1.0 ROUGE-L.