Few-Shot Learning for Abstractive Multi-Document Opinon Summarization
Table 8: ROUGE scores on the Amazon test set for alternative summary adaptation strategies.
['[EMPTY]', 'R1', 'R2', 'RL']
We further explored alternative utilization approaches of annotated data-points, based on the same split of the Amazon summaries as explained in Sec. First, we trained a model in the unsupervised learning setting (USL) on the Amazon reviews with the leave-one-out objective in Eq. In this setting, the model has no exposure to summaries, neither summary properties as the oracle q(ri,r−i) is not used. Further, we considered two alternative settings how the pre-trained unsupervised model can be adapted on the gold summaries. In the first setting, the pre-trained unsupervised model is further re-trained by predicting summaries conditioned on input reviews (USL+R). In the second one, similar to Hoang et al. Here USL is further trained on a mixture of unannotated corpus review and gold summary batches with domain learned embedding added at each position during prediction.