Few-Shot Learning for Abstractive Multi-Document Opinon Summarization
Table 9: Text characteristics of generated summaries by different models on the Amazon test set. The first column block corresponds to the POV marginal distribution; the second to the average length deviation (in words) from the human-created summaries, where a negative value corresponds to on average shorter generated text than summaries.
['[EMPTY]', '1st', '2nd', '3rd', 'NoPr', 'Len']
We further analyzed how a plain re-training on summaries differs from our approach in terms of summary characteristics adaptation. For comparison, we used USL and USL+R, which are presented in Sec. Additionally, we analyzed unannotated reviews from the Amazon training set. Specifically, we focused on text formality and the average word count difference (Len) from gold summaries in the Amazon gold test set. As a proxy for the former, we computed marginal distributions over points of view (POV) based on pronoun counts, additional class (NoPr) was allocated to cases of no pronouns, same as described in Sec.