Mobile IoT device summarizer using P2P web search engine and inherent characteristic of contents | Peer-to-Peer Networking and Applications Skip to main content

Advertisement

Springer Nature Link
Log in

Mobile IoT device summarizer using P2P web search engine and inherent characteristic of contents

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Grasping the sense of web pages on small screen of mobile devices is too difficult, so mobile users prefer summarized reports. One popular technique to generate automatic summarized report is based on vector model. The vector model summarization methods are simple and low cost to implement on mobile devices. However, the quality of summarized reports of the model may not be good because there is a semantic difference between manual summarized reports and the machine summarized reports. In addition, the censored documents of the centralized search engine cannot accurately reflect the query of user in the summary results. To overcome this constraints, automatic mobile device summarizer using a semantic feature of pseudo relevance feedback and P2P web search engine is proposed. The proposed method increases the quality of summarized reports by using the latent features of documents and clustering technique. The method uses none censored documents of the P2P web search engine to more accurately reflect user requirements in summarized reports. The automatic summarizer consists of a server-side containerized auto-summary module for flexible management of the summarization functions and a user-side mobile device module to reduce the overload of the mobile device.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Liu D, Wu S, Lan Y (2013) A query-oriented XML text summarization for mobile devices. Soft Comput 17:1585–1593

    Article  Google Scholar 

  2. Yazhini R, Vishnu Raja P (2014) Automatic summarizer for Mobile devices using sentence ranking measure. In: Proc. international conference on recent trends in information technology

  3. Ricardo BY, Berthier RN (1999) Modern information retrieval. ACM Press

  4. Han KS, Bea DH, Rim HC (2017) Automatic text summarization based on relevance feedback with query splitting. J Adv Comput Sci Appl 8(10):397–405

    Google Scholar 

  5. Allahyari M, Pourieh S, Assefi M et al (2008) Text summarization techniques: a brief survey. In: Proc. annual international ACM SIGIR conference on research and development in information retrieval, Singapore, pp 291–298

  6. Park S (2012) Personalized document summarization using pseudo relevance feedback and semantic feature. IETE J Res 58(2):155–165

    Article  Google Scholar 

  7. Park S (2010) Automatic multi-document summarization based on clustering and non-negative matrix factorization. IETE Tech Rev 27(2):167–178

    Article  Google Scholar 

  8. Park S, Cha BR, Kim JW (2016) Document summarization using NMF and pseudo relevance feedback based on K-means clustering. Comput Inform 35(3):744–760

  9. Porkaev K, Chakrabarti K, Mehtotra S (1999) Query refinement for multimedia similarity retrieval in MARS. In: Proc. annual ACM international conference on multimedia, Los Angeles, pp 235–238

  10. Park S, Lee JH, Ahn CM, Hong JS, Chun SJ (2006) Query based summarization using non-negative matrix factorization, vol 4253. Springer, LNAI, pp 84–87

    Google Scholar 

  11. Park S, Lee JH, Kim DH, Ahn CM (2007) Multi-document summarization based on cluster using non-negative matrix factorization, vol 4363. Springer, LNCS, pp 761–770

    Google Scholar 

  12. Park S, Lee JH, Kim DH, Ahn CM (2007) Multi-document summarization using weighted similarity between topic and clustering-based non-negative matrix factorization, vol 4505. Springer, LNCS, pp 108–115

  13. Wang D, Li T, Zhu S, Ding C (2008) Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. In: Proc. annual international ACM SIGIR conference on research and development in information retrieval, Singapore, pp 307–314

  14. Nallapati R, Zhou B, Santos CD (2016) Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proc. SIGNLL conference on computational natural language learning, pp 280–290

  15. Khatri C, Singh G, Parikh N (2018) Abstractive and extractive text summarization using document context vector and recurrent neural networks. In: Proc. KDD 2018 deep learning day, London, UK

  16. Song S, Huang H, Ruan T (2019) Abstractive text summarization using LSTM-CNN based deep learning. Multimed Tools Appl 78:857–874

    Article  Google Scholar 

  17. Radev DR, Jing H, Stys M, Tam D (2004) Centroid-based summarization of multiple documents. Inf Process Manag 40(6):919–938

    Article  Google Scholar 

  18. Afantenos S, Karkaletsis V, Stamatopoulos P (2005) Summarization from medical documents: a survey. Artif Intell Med 33(2):157–177

    Article  Google Scholar 

  19. Jones KS (2007) Automatic summarising: the state of the art. Inf Process Manag 43(6):1449–1481

    Article  Google Scholar 

  20. Sanderson M (1998) Accurate user directed summarization from existing tools. In: Proc. international conference on information and knowledge management, CIKM’98, Bethesda Maryland, pp 45–51

  21. Varadarajan R, Hristidis V (2005) Structure-based query-specific document summarization. In: Proc. international conference on information and knowledge management, Bremen Germany, pp 231–232

  22. Garcia LFF, Lima JVD, Loh S (2007) Using ontological modelling in a context-aware summarization system to adapt text for Mobile device, vol 5412. Springer, LNCS, pp 144–154

    Google Scholar 

  23. Jung H, Chung K (2016) Knowledge-based dietary nutrition recommendation for obese management. Inf Technol Manag 17(1):29–42

    Article  Google Scholar 

  24. Kim SH, Chung K (2016) Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust Comput 18(2):747–759

    Article  Google Scholar 

  25. Chung K, Na Y, Lee JH (2013) Interactive design recommendation using sensor based smart Wear and weather WebBot. Wirel Pers Commun 73(2):243–256

    Article  Google Scholar 

  26. Chung K, Park RC (2016) PHR open platform based smart health service using distributed object group framework. Clust Comput 19(1):505–517

    Article  Google Scholar 

  27. Kim JC, Chung K (2017) Depression index service using knowledge based crowdsourcing in smart health. Wirel Pers Commun 93(1):255–268

    Article  Google Scholar 

  28. Yoo H, Chung K (2017) PHR based diabetes index service model using life behavior analysis. Wirel Pers Commun 93(1):161–174

    Article  Google Scholar 

  29. Herrmann M, Zhang R, Ning KC, Diaz C, Preneel B (2014) Censorship-resistant and privancy-preserving distributed web search. In: Proc. 14th IEEE international conference on peer-to-peer computing

  30. Kim J, Chung K (2018) Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Netw Appl 11(6):1278–1287

    Article  Google Scholar 

  31. Kim SH, Chung K (2015) Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust Comput 18(2):747–759

    Article  Google Scholar 

  32. Chung K, Kim JC, Park RC (2016) Knowledge-based health service considering user convenience using hybrid Wi-fi P2P. Inf Technol Manag 17(1):67–80

    Article  Google Scholar 

  33. Herrmann M, Zhang R, Ning KC, Diaz C, Preneel B (2014) Descriptino of the YaCy distributed web search engine. Technicla report. KU Leuven ESAT/COSIC, iMinds

  34. YaCY. https://yacy.net/en/index.html. Accessed on: 2019

  35. Scott H Software containers: used more frequently than most realize. Network World, Inc. [Online] Available: https://www.networkworld.com/article/2226996/cisco-subnet/software-containers%2D%2Dused-more-frequently-than-most-realize.html. Accessed on: 2018

  36. Lin CY (2004) ROUGE: a package for automatic evaluation of summaries. In: Proc. workshop on text summarization branches out, post-conference workshop of ACL, Barcelona Spain

Download references

Acknowledgements

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2016R1D1A1B03934823).

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, GIST, and GenoTech, Gwangju, 61005, South Korea

    Sun Park & ByungRea Cha

  2. Division of Computer Science and Engineering, Kyonggi University, 15442, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16227, South Korea

    Kyungyong Chung

  3. School of Electrical Engineering and Computer Science, GIST, Gwangju, 61005, South Korea

    JongWon Kim

Authors
  1. Sun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ByungRea Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kyungyong Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. JongWon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sun Park.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on P2P Computing for Intelligence of Things

Guest Editors: Sunmoon Jo, Jieun Lee, Jungsoo Han, and Supratip Ghose

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, S., Cha, B., Chung, K. et al. Mobile IoT device summarizer using P2P web search engine and inherent characteristic of contents. Peer-to-Peer Netw. Appl. 13, 684–693 (2020). https://doi.org/10.1007/s12083-019-00780-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-019-00780-w

Keywords

Search

Navigation