Automatically Evaluating Opinion Prevalence in Opinion Summarization
This work addresses the challenge of automatically assessing opinion representativeness in product review summaries, which is incremental as it builds on existing consistency metrics and applies them to a specific domain.
The authors tackled the problem of evaluating opinion prevalence in opinion summarization by proposing an automatic metric based on counting consistent reviews and discrediting trivial statements, showing that human-authored summaries only slightly outperform random extracts and that preprocessing with simplification can raise existing systems to human performance levels.
When faced with a large number of product reviews, it is not clear that a human can remember all of them and weight opinions representatively to write a good reference summary. We propose an automatic metric to test the prevalence of the opinions that a summary expresses, based on counting the number of reviews that are consistent with each statement in the summary, while discrediting trivial or redundant statements. To formulate this opinion prevalence metric, we consider several existing methods to score the factual consistency of a summary statement with respect to each individual source review. On a corpus of Amazon product reviews, we gather multiple human judgments of the opinion consistency, to determine which automatic metric best expresses consistency in product reviews. Using the resulting opinion prevalence metric, we show that a human authored summary has only slightly better opinion prevalence than randomly selected extracts from the source reviews, and previous extractive and abstractive unsupervised opinion summarization methods perform worse than humans. We demonstrate room for improvement with a greedy construction of extractive summaries with twice the opinion prevalence achieved by humans. Finally, we show that preprocessing source reviews by simplification can raise the opinion prevalence achieved by existing abstractive opinion summarization systems to the level of human performance.