{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:03:18Z","timestamp":1768208598844,"version":"3.49.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>We address personalized review summarization, which generates a condensed summary for a user\u2019s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users\u2019 characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536,255 reviews from 19,400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.33016690","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T07:37:29Z","timestamp":1566805049000},"page":"6690-6697","source":"Crossref","is-referenced-by-count":26,"title":["Towards Personalized Review Summarization via User-Aware Sequence Network"],"prefix":"10.1609","volume":"33","author":[{"given":"Junjie","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqing","family":"Zong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4640\/4518","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4640\/4518","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T06:34:23Z","timestamp":1667802863000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.33016690","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}