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.

本研究提出了 SUBSUMM，一种用于大规模多角度意见摘要的监督式摘要框架，实验证明该框架能够从数百个输入的评论中生成正面、负面和结论性摘要，并且深度分析表明评论子集的先进选择和两阶段训练方案对提高摘要性能至关重要。