  Evaluation of opinion summaries using conventional reference-based metrics
rarely provides a holistic evaluation and has been shown to have a relatively
low correlation with human judgments. Recent studies suggest using Large
Language Models (LLMs) as reference-free metrics for NLG evaluation, however,
they remain unexplored for opinion summary evaluation. Moreover, limited
opinion summary evaluation datasets inhibit progress. To address this, we
release the SUMMEVAL-OP dataset covering 7 dimensions related to the evaluation
of opinion summaries: fluency, coherence, relevance, faithfulness, aspect
coverage, sentiment consistency, and specificity. We investigate Op-I-Prompt a
dimension-independent prompt, and Op-Prompts, a dimension-dependent set of
prompts for opinion summary evaluation. Experiments indicate that Op-I-Prompt
emerges as a good alternative for evaluating opinion summaries achieving an
average Spearman correlation of 0.70 with humans, outperforming all previous
approaches. To the best of our knowledge, we are the first to investigate LLMs
as evaluators on both closed-source and open-source models in the opinion
summarization domain.
