World Wide Web https://doi.org/10.1007/s11280-018-0597-7 MARES: multitask learning algorithm for Web-scale real-time event summarization 1 2 1 3 Min Yang · Wenting Tu · Qiang Qu · Kai Lei · 4 5 6 Xiaojun Chen · Jia Zhu · Ying Shen Received: 5 October 2017 / Revised: 6 February 2018 / Accepted: 24 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Automatic real-time summarization of massive document streams on the Web has become an important tool for quickly transforming theoverwhelming documents into a novel, comprehensive and concise overview of an event for users. Significant progresses have been made in static text summarization. However, most previous work does not con- sider the temporal features of the document streams which are valuable in real-time event summarization. In this paper, we propose a novel Multitask learning Algorithm for Web- This article belongs to the Topical Collection: Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications Guest Editors: Jingkuan Song, Shuqiang Jiang, Elisa Ricci, and Zi Huang Jia Zhu firstname.lastname@example.org Min Yang email@example.com Wenting Tu firstname.lastname@example.org Qiang Qu email@example.com Kai Lei firstname.lastname@example.org Xiaojun Chen email@example.com Ying Shen firstname.lastname@example.org Shenzhen Institutes of Advanced Technology, Chinese
World Wide Web – Springer Journals
Published: Jun 2, 2018
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