Jointly Extracting and Compressing Documentswith Summary State Representations
Table 5: Summary state ablation for the CNN dataset.
['State', 'ROUGE R1', 'ROUGE R2', 'ROUGE RL']
Next, we performed an ablation study to investigate the importance of the summary state representation osi w.r.t. the quality of the overall summary. We tested against a State averaging variant, where we replace osi by a weighted average, analogous to \newciteNallapati2017SummaRuNNer, oavg si=∑j−1i=1e(si)p(zi∣pavgi), where pavgi has the same form as pi but depends recursively on the previous summary state oavg si−1. The other ablation study shows how learning to extract and compress in a disjoint approach (ExConSumm Ext+Comp oracle) performs against a joint learning approach (ExConSumm Compressive). We compared summaries generated from our best extractive model and compressed them with a compressive oracle. Our joint learning model achieves the best performance in all metrics compared with the other ablations, suggesting that joint learning and using a summary state representation is beneficial for summarization.