Iterative Document Representation Learning Towards Summarization with Polishing
Table 1: Comparison with other baselines on DailyMail test dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes and at 275 bytes.
['DailyMail', 'Rouge-1', 'b75 Rouge-2', 'Rouge-L', 'Rouge-1', 'b275 Rouge-2', 'Rouge-L']
Our model performs consistently and significantly better than other models on 75 bytes, while on 275 bytes, the improvement margin is smaller. One possible interpretation is that our model has high precision on top rank outputs, but the accuracy is lower for lower rank sentences.