MARES: multitask learning algorithm for Web-scale real-time event summarization

MARES: multitask learning algorithm for Web-scale real-time event summarization 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 jzhu@m.scnu.edu.cn Min Yang min.yang1129@gmail.com Wenting Tu tu.wenting@mail.shufe.edu.cn Qiang Qu qiang.qu@siat.ac.cn Kai Lei leik@pkusz.edu.cn Xiaojun Chen xjchen@szu.edu.cn Ying Shen shenying@pkusz.edu.cn Shenzhen Institutes of Advanced Technology, Chinese http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png World Wide Web Springer Journals

MARES: multitask learning algorithm for Web-scale real-time event summarization

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Information Systems Applications (incl.Internet); Database Management; Operating Systems
ISSN
1386-145X
eISSN
1573-1413
D.O.I.
10.1007/s11280-018-0597-7
Publisher site
See Article on Publisher Site

Abstract

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 jzhu@m.scnu.edu.cn Min Yang min.yang1129@gmail.com Wenting Tu tu.wenting@mail.shufe.edu.cn Qiang Qu qiang.qu@siat.ac.cn Kai Lei leik@pkusz.edu.cn Xiaojun Chen xjchen@szu.edu.cn Ying Shen shenying@pkusz.edu.cn Shenzhen Institutes of Advanced Technology, Chinese

Journal

World Wide WebSpringer Journals

Published: Jun 2, 2018

References

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