Abstractive Document Summarization without Parallel Data
Table 4: Metric results on the Scientific summarization 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.
['2  [BOLD] Approach', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L', '[BOLD] MET', '[BOLD] #']
Both of our abstractive models outperformed their respective extractive baselines, indicating that our abstractors are beneficial for this task and have learned useful sentence transformations. The summaries are much shorter after abstraction, indicating that the paraphrased summaries are meaningfully compressed. Surprisingly, the gap between the baselines and the oracle is small, indicating that abstractive summarization is essential for achieving good performance on this task.