Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

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

  • 314 Accesses

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.

This is a preview of subscription content, log in to check access.

Figure 1

Notes

  1. 1.

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

  2. 2.

    http://trecrts.github.io/

  3. 3.

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

  4. 4.

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

  5. 5.

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

References

  1. 1.

    Aliannejadi, M., Bahrainian, S.A., Giachanou, A., Crestani, F.: University of lugano at trec 2015: Contextual suggestion and temporal summarization tracks. In: TREC (2015)

  2. 2.

    Allan, J., Gupta, R., Khandelwal, V.: Temporal summaries of new topics. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 10–18. ACM (2001)

  3. 3.

    Aslam, J., Diaz, F., Ekstrand-Abueg, M., McCreadie, R., Pavlu, V., Sakai, T.: Trec 2014 temporal summarization track overview. Technical report (2015)

  4. 4.

    Cao, Z., Wei, F., Li, D., Li, S., Zhou, M.: Ranking with recursive neural networks and its application to multi-document summarization. In: AAAI, pp. 2153–2159 (2015)

  5. 5.

    Cao, Z., Li, W., Li, S., Wei, F.: Improving multi-document summarization via text classification. In: AAAI, pp. 3053–3059 (2017)

  6. 6.

    Chen, G.: A gentle tutorial of recurrent neural network with error backpropagation. arXiv:1610.02583 (2016)

  7. 7.

    Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. arXiv:1603.07252 (2016)

  8. 8.

    Deshpande, A.R., Lobo, L.M.R.J.: Text summarization using clustering technique. Int. J. Eng. Trends Technol., 4(8) (2013)

  9. 9.

    Efron, M., Lin, J., He, J., De Vries, A.: Temporal feedback for tweet search with non-parametric density estimation. In: SIGIR, pp. 33–42. ACM (2014)

  10. 10.

    Erkan, G., Radev, D.R.: Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)

  11. 11.

    Frank, J.R., Kleiman-Weiner, M., Roberts, D.A., Niu, F., Ce, Z., Ré, C., Soboroff, I.: Building an entity-centric stream filtering test collection for trec. Technical report. Massachusetts. Inst of. Tech. Cambridge. (2012)

  12. 12.

    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)

  13. 13.

    Galley, M.: A skip-chain conditional random field for ranking meeting utterances by importance. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp, 364–372. Association for Computational Linguistics (2006)

  14. 14.

    Gao, L., Guo, Z., Zhang, H., Xing, X., Shen, H.T.: Video captioning with attention-based lstm and semantic consistency. IEEE Trans. Multimed. 19(9), 2045–2055 (2017)

  15. 15.

    Gao, L., Song, J., Liu, X., Shao, J., Liu, J., Shao, J.: Learning in high-dimensional multimedia data: the state of the art. Multimed. Syst. 23(3), 303–313 (2017)

  16. 16.

    Gillick, D., Favre, B.: A scalable global model for summarization. In: Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing, pp. 10–18. Association for Computational Linguistics (2009)

  17. 17.

    Guo, Q., Diaz, F., Yom-Tov, E.: Updating users about time critical events. In: European Conference on Information Retrieval, pp. 483–494. Springer (2013)

  18. 18.

    He, H., Gimpel, K., Lin, J.J.: Multi-perspective sentence similarity modeling with convolutional neural networks. In: EMNLP, pp. 1576–1586 (2015)

  19. 19.

    Hovy, E., Lin, C.-Y.: Automated text summarization and the summarist system. In: Proceedings of a Workshop on Held at Baltimore, Maryland: October 13-15, 1998, pp. 197?214. Association for Computational Linguistics (1998)

  20. 20.

    Kågebäck, M, Mogren, O., Tahmasebi, N., Dubhashi, D.: Extractive summarization using continuous vector space models. In: Proceedings of the 2nd EACL Workshop on Continuous Vector Space Models and their Compositionality, pp. 31–39 (2014)

  21. 21.

    Kedzie, C., McKeown, K., Diaz, F.: Predicting salient updates for disaster summarization. In: ACL (1), pp. 1608–1617 (2015)

  22. 22.

    Kedzie, C., Diaz, F., McKeown, K.: Real-time Web scale event summarization using sequential decision making. In: International Joint Conference on Artificial Intelligence, pp. 3754–3760 (2016)

  23. 23.

    Kingma, D., Adam, J.B.: A method for stochastic optimization. arXiv:1412.6980 (2014)

  24. 24.

    Kruengkrai, C., Jaruskulchai, C.: Generic text summarization using local and global properties of sentences. In: IEEE/WIC International Conference on Web Intelligence, 2003. Proceedings, pp. 201–206. IEEE (2003)

  25. 25.

    Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 68–73. ACM (1995)

  26. 26.

    Li, C., Qian, X., Liu, Y.: Using supervised bigram-based ilp for extractive summarization. In: ACL, pp. 1004–1013 (2013)

  27. 27.

    Lin, J., Efron, M., Wang, Y., Sherman, G.: Overview of the trec-2015 microblog track. Technical report (2015)

  28. 28.

    Lin, J., Roegiest, A., Tan, L., McCreadie, R., Voorhees, E., Diaz, F.: Overview of the trec 2016 real-time summarization track. In: TREC (2016)

  29. 29.

    Liu, G., Yan, Y., Subramanian, R., Song, J., Guoyu, L., Sebe, N.: Active domain adaptation with noisy labels for multimedia analysis. World Wide Web 19(2), 199–215 (2016)

  30. 30.

    McCreadie, R., Macdonald, C., Ounis, I.: Incremental update summarization: adaptive sentence selection based on prevalence and novelty. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 301–310. ACM (2014)

  31. 31.

    McDonald, R.: A study of global inference algorithms in multi-document summarization. Adv. Inf. Retriev., 557–564 (2007)

  32. 32.

    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)

  33. 33.

    Rao, J., He, H., Zhang, H., Ture, F., Sequiera, R., Mohammed, S., Lin, J.: Integrating lexical and temporal signals in neural ranking models for searching social media streams. arXiv:1707.07792 (2017)

  34. 34.

    Roegiest, A., Tan, L., Lin, J.: Online in-situ interleaved evaluation of real-time push notification systems. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 415–424. ACM (2017)

  35. 35.

    Ross, S., Gordon, G.J., Bagnell, D.: A reduction of imitation learning and structured prediction to no-regret online learning. In: International Conference on Artificial Intelligence and Statistics, pp. 627–635 (2011)

  36. 36.

    Severyn, A., Moschitti, A.: Learning to rank short text pairs with convolutional deep neural networks. In: SIGIR, pp. 373–382. ACM (2015)

  37. 37.

    Sonawane, S.S., Kulkarni, P.A.: Graph based representation and analysis of text document: A survey of techniques. Int. J. Comput. Appl., 96(19) (2014)

  38. 38.

    Song, J., Gao, L., Nie, F., Shen, H.T., Yan, Y., Sebe, N.: Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans. Image. Process. 25(11), 4999–5011 (2016)

  39. 39.

    Song, J., Gao, L., Li, L., Zhu, X., Sebe, N.: Quantization-based hashing: A general framework for scalable image and video retrieval. Pattern Recognition (2017)

  40. 40.

    Song, J., Zhang, H., Li, X., Gao, L., Wang, M., Hong, R.: Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans. Image. Process. 25 (11), 4999–5011 (2018)

  41. 41.

    Tan, L., Roegiest, A., Clarke, C.L.A., Lin, J.: Simple dynamic emission strategies for microblog filtering. In: SIGIR, pp. 1009–1012. ACM (2016)

  42. 42.

    Tan, H., Ziyu, L., Li, W.: Neural network based reinforcement learning for real-time pushing on text stream. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 913–916. ACM (2017)

  43. 43.

    Wang, X., Gao, L., Wang, P., Sun, X., Liu, X.: Two-stream 3d convnet fusion for action recognition in videos with arbitrary size and length. IEEE Transactions on Multimedia (2017)

  44. 44.

    Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3-4), 229–256 (1992)

  45. 45.

    Xu, T., Oard, D.W., McNamee, P.: Hltcoe at trec 2013: Temporal summarization. In: TREC (2013)

  46. 46.

    Xu, J., Liu, X., Huo, Z., Deng, C., Nie, F., Huang, H.: Multi-class support vector machine via maximizing multi-class margins. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp. 3154–3160 (2017)

  47. 47.

    Yang, M., Mei, J., Fei, X., Wenting, T., Lu, Z.: Discovering author interest evolution in topic modeling. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 801–804. ACM (2016)

  48. 48.

    Yu, L., Zhang, W., Wang, J., Seqgan, Y.Y.: Sequence generative adversarial nets with policy gradient. In: AAAI, pp. 2852–2858 (2017)

  49. 49.

    Zhao, W., Wei, X., Yang, M., Ye, J., Zhao, Z., Feng, Y., Qiao, Y.: Dual learning for cross-domain image captioning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 29–38. ACM (2017)

  50. 50.

    Zhu, J., Xie, Q., Zheng, K.: An improved early detection method of type-2 diabetes mellitus using multiple classifier system. Inform. Sci. 292, 1–14 (2015)

  51. 51.

    Zhu, J., Xie, Q., Wong, W.H., Wong, W.H.: Exploiting link structure for Web page genre identification. Data. Min. Knowl. Disc. 30(3), 550–575 (2016)

  52. 52.

    Zhu, X., Suk, H.-I., Lee, S.-W., Shen, D.: Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2016)

Download references

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

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Multitask learning
  • Real-time event summarization
  • Relevance prediction
  • Document filtering