Windowing Models for Abstractive Summarization of Long Texts
Table 1: Results on the CNN/Dailymail test set: summaries of Ty=125 tokens; Stan trained with fixed-size input of Tx=400 tokens; SWM (d=1.2, k=0.8) & DWM trained on Tx=1160 tokens, with windows of Tw=400 tokens (stride ss=380).
['Model', 'R-1', 'R-2', 'R-L']
Unsurprisingly, the simple Lead-3 baseline outperforms Stan and both our static and dynamic windowing models. This is because in CNN/Dailymail documents almost all of the summary-relevant content is found at the very beginning of the document. The ability to process all windows does not benefit to SWM and DWM in this setting as there is virtually no summary-relevant content in later windows.