A Neural Attention Model for Abstractive Sentence Summarization
Table 1: Experimental results on the main summary tasks on various ROUGE metrics . Baseline models are described in detail in Section 7.2. We report the percentage of tokens in the summary that also appear in the input for Gigaword as Ext %.
['Model', 'DUC-2004 ROUGE-1', 'DUC-2004 ROUGE-2', 'DUC-2004 ROUGE-L', 'Gigaword ROUGE-1', 'Gigaword ROUGE-2', 'Gigaword ROUGE-L', 'Gigaword Ext. %']
We run experiments both using the DUC-2004 evaluation data set (500 sentences, 4 references, 75 bytes) with all systems and a randomly held-out Gigaword test set (2000 sentences, 1 reference). We first note that the baselines Compress and IR do relatively poorly on both datasets, indicating that neither just having article information or language model information alone is sufficient for the task. The Prefix baseline actually performs surprisingly well on ROUGE-1 which makes sense given the earlier observed overlap between article and summary.