Abstractive Text Summarization based onLanguage Model Conditioning and Locality Modeling
Table 6: ROUGE scores for our models on the SwissText test set
['Method', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Furthermore, we present the first publicly available benchmark for the SwissData dataset All parameters are equal to the CNN/Daily Mail baseline. BERT-conditioning was used only in the encoder. The networks were trained on the truncated texts in 90,000 training steps. From the results we see that the convolutional CopyTransformer showed much more efficiency than on CNN/Daily Mail dataset, outperforming the baseline by 1.04 percent on ROUGE-1, 1.26 on ROUGE-2 and 1.09 on ROUGE-L. The BERT-based model achieved the highest scores.