A Reinforced Topic-Aware Convolutional Sequence-to-SequenceModel for Abstractive Text Summarization
Table 6: Accuracy on the LCSTS dataset in terms of the full-length RG-1, RG-2, and RG-L. In last three rows, the word-level ROUGE scores are presented on the left and the character-level on the right.
['[EMPTY]', '[BOLD] RG-1 (F)', '[BOLD] RG-2 (F)', '[BOLD] RG-L (F)']
Since not all previous work explicitly mentioned whether word-based or character-based ROUGE metrics were reported, we evaluate our proposed model with both metrics in order to obtain a comprehensive comparison. We can also observe that the character-based results of our Reinforced-Topic-ConvS2S model outperforms every other method. Regarding to word-based ROUGE scores, our model obtains the best performance in terms of RG-1 and RG-L metrics. However, our best model does not achieve a good RG-2 score as its RG-1 and RG-L scores. We suspect that it may be partly caused by the biased probability generation mechanism that influences word order, which requires further studies.