Abstractive Document Summarization without Parallel Data
Table 1: Metric results on the CNN/Daily Mail test set. R-1/2/L are the ROUGE-1/2/L F1 scores; MET is METEOR, while # is the average number of tokens in the summaries. † are from [Chen and Bansal2018].
['2  [BOLD] Approach', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L', '[BOLD] MET', '[BOLD] #']
However, the results are promising: for example, the ROUGE-L gap between our Lead model and the supervised LSTM model is only 1.8. When comparing against the Ext-Abs and Ext-Abs-RL models, which perform supervised sentence extraction followed by supervised sentence abstraction, the gap is larger. Furthermore, we observe that, on this dataset, applying our abstractors to the Lead and LexRank extractive baselines leads to a decrease in ROUGE. This could be due to the much shorter length of our abstractive summaries in comparison to the length produced by other systems, indicating that our systems potentially summarize much more aggressively.