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World Wide Web

, Volume 22, Issue 2, pp 499–515 | Cite as

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

  • Min Yang
  • Wenting Tu
  • Qiang Qu
  • Kai Lei
  • Xiaojun Chen
  • Jia ZhuEmail author
  • Ying Shen
Article
  • 232 Downloads
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

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 consider the temporal features of the document streams which are valuable in real-time event summarization. In this paper, we propose a novel M ultitask learning A lgorithm for Web-scale R eal-time E vent S ummarization (MARES), which leverages the benefits of supervised deep neural networks as well as a reinforcement learning algorithm to strengthen the representation learning of documents. Specifically, MARES consists two key components: (i) A relevance prediction classifier, in which a hierarchical LSTM model is used to learn the representations of queries and documents; (ii) A document filtering model learns to maximize the long-term rewards with reinforcement learning algorithm, working on a shared document encoding layer with the relevance prediction component. To verify the effectiveness of the proposed model, extensive experiments are conducted on two real-life document stream datasets: TREC Real-Time Summarization Track data and TREC Temporal Summarization Track data. The experimental results demonstrate that our model can achieve significantly better results than the state-of-the-art baseline methods.

Keywords

Multitask learning Real-time event summarization Relevance prediction Document filtering 

Notes

Acknowledgements

This work was partially supported by the National Science Foundation of China (No.61750110516), the Shenzhen Key Fundamental Research Projects (Grant No. JCYJ20170412150946024), and the CAS Pioneer Hundred Talents Program.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Min Yang
    • 1
  • Wenting Tu
    • 2
  • Qiang Qu
    • 1
  • Kai Lei
    • 3
  • Xiaojun Chen
    • 4
  • Jia Zhu
    • 5
    Email author
  • Ying Shen
    • 6
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Department of Computer ScienceShanghai University of Finance and EconomicsShanghaiChina
  3. 3.School of Electronics and Computer EngineeringPeking UniversityShenZhenChina
  4. 4.School of Computing ScienceShenzhen UniversityShenzhenChina
  5. 5.School of Computing ScienceSouth China Normal UniversityGuangzhouChina
  6. 6.School of Electronics and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhenChina

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