Large-Scale and Multi-Perspective Opinion Summarization with Diverse
  Review Subsets
Han Jiang Rui Wang Zhihua Wei Yu Li Xinpeng Wang
Opinion summarization is expected to digest larger review sets and provide
summaries from different perspectives. However, most existing solutions are
deficient in epitomizing extensive reviews and offering opinion summaries from
various angles due to the lack of designs for information selection. To this
end, we propose SUBSUMM, a supervised summarization framework for large-scale
multi-perspective opinion summarization. SUBSUMM consists of a review sampling
strategy set and a two-stage training scheme. The sampling strategies take
sentiment orientation and contrastive information value into consideration,
with which the review subsets from different perspectives and quality levels
can be selected. Subsequently, the summarizer is encouraged to learn from the
sub-optimal and optimal subsets successively in order to capitalize on the
massive input. Experimental results on AmaSum and Rotten Tomatoes datasets
demonstrate that SUBSUMM is adept at generating pros, cons, and verdict
summaries from hundreds of input reviews. Furthermore, our in-depth analysis
verifies that the advanced selection of review subsets and the two-stage
training scheme are vital to boosting the summarization performance.