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Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study \u201chow to generate a comprehensive review summary from a large number of user-generated reviews.\u201d This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive\n            <jats:italic>Review Summary Generation<\/jats:italic>\n            frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users\u2019 sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.\n          <\/jats:p>","DOI":"10.1145\/3448015","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T19:36:14Z","timestamp":1620934574000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4985-7454","authenticated-orcid":false,"given":"Wenjun","family":"Jiang","sequence":"first","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Ding","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[{"name":"Temple University, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"He","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojun","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangzhou University, Guangzhou, Guangdong Province, P. 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