Attention Optimization for Abstractive Document Summarization
Table 1: Performance on CNN/Daily Mail test dataset.
['[BOLD] Models', '[BOLD] ROUGE-1', '[BOLD] ROUGE-2', '[BOLD] ROUGE-L', '[BOLD] METEOR']
DCA Celikyilmaz et al. Although our experimental results have not outperformed the state-of-the-art models, our model has a much simpler structure with fewer parameters. Besides, these simple methods do yield a boost in performance compared with PGN baseline and may be applied on other models with attention mechanism. We further evaluate how these optimization approaches work. The ARU module has achieved a gain of 0.97 ROUGE-1, 0.35 ROUGE-2, and 0.64 ROUGE-L points; the local variance loss boosts the model by 3.01 ROUGE-1, 1.6 ROUGE-2, and 2.58 ROUGE-L.