While extractive techniques are generally preferred over abstractive ones (as abstraction can introduce disfluency), existing extractive summarizers are either supervised or based on heuristics of certain desired characteristics of the summarization result (e.g., maximize n-gram coverage (Nenkova and Vanderwende, 2005), etc.).
when it comes to online reviews, there are problems with both approaches: the first one requires manual annotation and is thus less generalizable; the second one might not capture the salient information in reviews from different domains (camera reviews vs. movie reviews), because the heuristics are designed for traditional genres (e.g., news articles) while the utility of reviews might vary with the review domain.