We propose a novel unsupervised extractive approach for summarizing online reviews by exploiting review helpfulness ratings. In addition to using the helpfulness ratings for review-level filtering, we suggest using them as the supervision of a topic model for sentence-level content scoring. The proposed method is metadata-driven, requiring no human annotation, and generalizable to different kinds of online reviews. Our experiment based on a widely used multi-document summarization framework shows that our helpfulness-guided review summarizers significantly outperform a traditional content-based summarizer in both human evaluation and automated evaluation.