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

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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.

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Notes

  1. An example of text queries can be found in the experimental part.

  2. http://trecrts.github.io/

  3. http://www.trec-ts.org/

  4. https://github.com/castorini/SM-CNN-Torch

  5. https://github.com/castorini/MP-CNN-Torch

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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.

Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Min Yang & Qiang Qu

  2. Department of Computer Science, Shanghai University of Finance and Economics, Shanghai, China

    Wenting Tu

  3. School of Electronics and Computer Engineering, Peking University, ShenZhen, China

    Kai Lei

  4. School of Computing Science, Shenzhen University, Shenzhen, China

    Xiaojun Chen

  5. School of Computing Science, South China Normal University, Guangzhou, China

    Jia Zhu

  6. School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China

    Ying Shen

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  1. Min Yang
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  2. Wenting Tu
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  3. Qiang Qu
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  4. Kai Lei
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  5. Xiaojun Chen
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  6. Jia Zhu
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  7. Ying Shen
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Correspondence to Jia Zhu.

Additional information

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

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Yang, M., Tu, W., Qu, Q. et al. MARES: multitask learning algorithm for Web-scale real-time event summarization. World Wide Web 22, 499–515 (2019). https://doi.org/10.1007/s11280-018-0597-7

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