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When faced with a large number of product reviews, it is not clear that ahuman can remember all of them and weight opinions representatively to write agood reference summary. We propose an automatic metric to test the prevalenceof the opinions that a summary expresses, based on counting the number ofreviews that are consistent with each statement in the summary, whilediscrediting trivial or redundant statements. To formulate this opinionprevalence metric, we consider several existing methods to score the factualconsistency of a summary statement with respect to each individual sourcereview. On a corpus of Amazon product reviews, we gather multiple humanjudgments of the opinion consistency, to determine which automatic metric bestexpresses consistency in product reviews. Using the resulting opinionprevalence metric, we show that a human authored summary has only slightlybetter opinion prevalence than randomly selected extracts from the sourcereviews, and previous extractive and abstractive unsupervised opinionsummarization methods perform worse than humans. We demonstrate room forimprovement with a greedy construction of extractive summaries with twice theopinion prevalence achieved by humans. Finally, we show that preprocessingsource reviews by simplification can raise the opinion prevalence achieved byexisting abstractive opinion summarization systems to the level of humanperformance.

Automatically Evaluating Opinion Prevalence in Opinion Summarization