Neural Summarization by Extracting Sentences and Words
Table 1: Rouge evaluation (%) on the DUC-2002 and 500 DailyMail samples.
['DUC 2002', 'Rouge-1', 'Rouge-2', 'Rouge-l']
nn-se represents our neural sentence extraction model, nn-we our word extraction model, and nn-abs the neural abstractive baseline. The table also includes results for the lead baseline, the logistic regression classifier (lreg), and three previously published systems (ilp, tgraph, and urank). In general, we observe similar trends to DUC 2002, with nn-se performing the best in terms of all rouge metrics. Note that scores here are generally lower compared to DUC 2002. This is due to the fact that the gold standard summaries (aka highlights) tend to be more laconic and as a result involve a substantial amount of paraphrasing. More experimental results on this dataset are provided in the appendix.