Abstractive Document Summarization without Parallel Data
Table 3: Comparison of abstractors trained on parallel (Abspar) vs. pseudo-parallel data (Abspp-θs, using different sentence alignment thresholds θs; Abspp-ub is the upper bound for large-scale alignment) or using a mixture of pseudo-parallel and synthetic data (Abspp+syn-N, using the Abspp-0.63 dataset and backtranslated data from the top N beam hypotheses). We always use the Lead extractor.
['2  [BOLD] Approach (# pairs)', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L', '[BOLD] #']
Abspp- *) as well as on a mixture of pseudo-parallel and backtranslated data ( Abspp+syn-*). For reference, we also include results from aligning the original dataset of CNN/DM articles and summaries directly. We construct a parallel dataset (Abspar) of sentence pairs by aligning the original CNN/DM document pairs using ROUGE-1; as well as a pseudo-parallel dataset (Abspp-ub) by applying the large-scale alignment method to the CNN/DM documents, (without using the document labels directly). The performance difference between Abspar and Abspp-ub provides an estimate of the performance loss due to pseudo alignment.