Time-aware Large Kernel Convolutions
Table 4: Results on CNN-DailyMail abstractive summarization.
['Model', 'Param', 'Rouge-1', 'Rouge-2', 'Rouge-L']
We evaluated the proposed method on the task of abstractive summarization. We test the method’s ability to process long documents on the CNN-DailyMail dataset. We encode an article of up to 400 sub-words and we generate a summarization composed from multiple sentences. Our Standard model is able to achieve better results on the Rouge-1 and Rouge-2 metrics than previous methods. In addition, the Standard model is using significantly less parameters, approximately 30M parameters less. The Deep model uses more layers to closely match the number of parameters and is able to outperform all previous models. This shows that our method is able to encode long sequences successfully without having the need to have access to all context.