We propose Topic Anchoring-based Review Summarization (TARS), a two-step extractive summarization method, which creates review summaries from the sentences that represent the most important aspects of a review. In the rst step, the proposed method utilizes Topic Aspect Sentiment Model (TASM), a novel sentiment-topic model, to identify aspects of sentiment-specic topics in a collection of reviews. e output of TASM is utilized in the second step of TARS to rank review sentences based on how representative of the most important review aspects their words are. alitative and quantitative evaluation of review summaries using two collections indicate the eectiveness of structuring review summaries around aspects of sentiment-specic topics
