Advertisement

Text Summarization by Hybridization of Hypergraphs and Hill Climbing Technique

  • Hemamalini Siranjeevi
  • Swaminathan VenkatramanEmail author
  • Kannan Krithivasan
Conference paper
  • 30 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)

Abstract

Automatic text summarization (ATS) is an application of natural language processing (NLP). It is the process of compressing the given text to create a summary. The challenge is creating a concise, non-redundant, coherent and inclusive summary that features all the significant points of the given text. The two approaches of summarization are extractive and abstractive. Extractive summarization works by choosing important sentences of the original text. It relies on the statistical relationship between the sentences. Since the sentences of the text are related by n-ary relationships, the authors have used these relationships to constitute the hyperedges of a hypergraph, which is called the sentence hypergraph. Hill climbing is an optimization technique that the authors have chosen to construct the sentence hypergraph. They succeeded in using the Helly property to select the significant sentences as summary. They have evaluated the performance of the system against the Gold summary using the ROUGE evaluation system.

Keywords

Automatic text summarization Extractive summarization Hypergraph Hill climbing Helly property ROUGE Sentence hypergraph 

Notes

Acknowledgements

The authors would like to thank the Management of SASTRA Deemed University and the Department of Science and Technology—Fund for Improvement of Science and Technology Infrastructure in Universities and higher educational institutions, Government of India, SR/FST/MSI-107/2015. The authors would like to thank the TATA realty Srinivasa Ramanujan Research cell.

References

  1. 1.
    Z.-K. Gao, Y.-X. Yang, P.-C. Fang, Y. Zou, C.-Y. Xia, D. Meng, Multiscale complex network for analyzing experimental multivariate time series. EPL (Europhys. Lett.) 109(3), 30005 (2015)CrossRefGoogle Scholar
  2. 2.
    Gerhard Weikum, Foundations of statistical natural language processing. ACM SIGMOD Record 31(3), 37 (2002)CrossRefGoogle Scholar
  3. 3.
    R. Ferreira, L. de Souza Cabral, R.D. Lins, G. Pereira e Silva, F. Freitas, G.D.C Cavalcanti, R. Lima, S.J. Simske, L. Favaro, Assessing sentence scoring techniques for extractive text summarization. Expert Syst. Appl. 40(14), 5755–5764 (2013)CrossRefGoogle Scholar
  4. 4.
    K. Mckeown, in Chapter 3 A Survey of Text Summarization (Springer, 2012)Google Scholar
  5. 5.
    J.V. Tohalino, D.R. Amancio, Extractive multi-document summarization using multilayer networks. Phys. A Stat. Mech. Appl. 503, 526–539 (2018)CrossRefGoogle Scholar
  6. 6.
    D. Yu, W. Wang, S. Zhang, W. Zhang, R. Liu, Hybrid self-optimized clustering model based on citation links and textual features to detect research topics. PLoS ONE (2017)Google Scholar
  7. 7.
    L. Marujo, W. Ling, R. Ribeiro, A. Gershman, J. Carbonell, D. Martins, D. Matos, J. Neto, Knowledge-based systems exploring events and distributed representations of text in multi-document summarization 94, 33–42 (2016)CrossRefGoogle Scholar
  8. 8.
    M.A. Fattah, A hybrid machine learning model for multi-document summarization. Appl. Intell. 40(4), 592–600 (2014)CrossRefGoogle Scholar
  9. 9.
    Z. Cao, F. Wei, L. Dong, S. Li, M. Zhou, Ranking with recursive neural networks and its application to multi-document summarization, in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI ’15 (AAAI Press, 2015), pp. 2153–2159Google Scholar
  10. 10.
    M. Yousefi-azar, Len Hamey, Text summarization using unsupervised deep learning. Expert Syst. Appl. 68, 93–105 (2017)CrossRefGoogle Scholar
  11. 11.
    H.P. Luhn, The automatic creation of literature abstracts. IBM J. Res. Develop. 2(2), 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  12. 12.
    R. Brandow, K. Mitze, L.F. Rau, Automatic condensation of electronic publications by sentence selection. Inf. Process. Manage. 31(5), 675–685 (1995)CrossRefGoogle Scholar
  13. 13.
    Y. Ko, J. Park, J. Seo, Automatic text categorization using the importance of sentences. Technical Report (2002)Google Scholar
  14. 14.
    D.R. Radev, LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)Google Scholar
  15. 15.
    R. Mihalcea, P. Tarau, Textrank: bringing order into text 85, 4040-4411 (2004)Google Scholar
  16. 16.
    H. Dalianis, M. Hassel, Swesum—automatic text summarizer. http://swesum.nada.kth.se/index-eng-adv.html. Accessed 15 Mar 2017 (2004)
  17. 17.
    L. Reeve, H. Han, A.D. Brooks, BioChain: lexical chaining methods for biomedical text summarization, pp. 23–27 (2006)Google Scholar
  18. 18.
    Y. Ko, J. Seo, An effective sentence-extraction technique using contextual information and statistical approaches for text summarization. Pattern Recognit. Lett. 29(9), 1366–1371 (2008)CrossRefGoogle Scholar
  19. 19.
    H. Saggion, A robust and adaptable summarization tool 49, 103–125 (2008)Google Scholar
  20. 20.
    L. Yang, X. Cai, Y. Zhang, Peng Shi, Enhancing sentence-level clustering with ranking-based clustering framework for theme-based summarization. Inf. Sci. 260, 37–50 (2014)CrossRefGoogle Scholar
  21. 21.
    E. Baralis, L. Cagliero, N. Mahoto, A. Fiori, GraphSum: discovering correlations among multiple terms for graph-based summarization. Inf. Sci. 249, 96–109 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    R. Mohana, A. Kukkar, An optimization technique for unsupervised automatic extractive bug report summarization, in Proceedings of the International Conference on Innovative Computing and Communications Lecture Notes in Networks and Systems, vol. 56 (2018), pp. 1–11Google Scholar
  23. 23.
    H. Ceylan, R. Mihalcea, U. Öyertem, E. Lloret, M. Palomar, Quantifying the limits and success of extractive summarization systems across domains. Human Lang. Technol. 903–911 (2010)Google Scholar
  24. 24.
    S. Gerani, G. Carenini, R.T Ng, Modeling content and structure for abstractive review summarization. Comput. Speech Lang. (2016)Google Scholar
  25. 25.
    W.M. Wang, Z. Li, J.W. Wang, Z.H. Zheng, How far we can go with extractive text summarization? Heuristic methods to obtain near upper bounds. Expert Syst. Appl. 90, 439–463 (2017)CrossRefGoogle Scholar
  26. 26.
    R. Rautray, R.C. Balabantaray, An evolutionary framework for multi document summarization using Cuckoo search approach: MDSCSA. Appl. Comput. Inf. 14(2), 134–144 (2018)CrossRefGoogle Scholar
  27. 27.
    R. Abbasi-ghalehtaki, H. Khotanlou, M. Esmaeilpour, Fuzzy evolutionary cellular learning automata model for text summarization. Swarm Evolut. Comput. 30, 11–26 (2016)CrossRefGoogle Scholar
  28. 28.
    F.B. Goularte, S.M. Nassar, R. Fileto, H. Saggion, A text summarization method based on fuzzy rules and applicable to automated assessment. Expert Syst. Appl. (2018)Google Scholar
  29. 29.
    R. Battiti, M. Brunato, F. Mascia, Reactive search and intelligent optimization, in Operations Research/Computer Science Interfaces Series (2009)Google Scholar
  30. 30.
    G. Kendall, E.K. Burke. Search methodologies. Technical Report (2005)Google Scholar
  31. 31.
    E.S. Tellez, D. Moctezuma, S. Miranda-Jiménez, M. Graff, An automated text categorization framework based on hyperparameter optimization. Knowl. Syst. 149, 110–123 (2018)CrossRefGoogle Scholar
  32. 32.
    C. Berge, HYPERGRAPHS combinatorics of finite sets. Technical Report (1989)Google Scholar
  33. 33.
    A. Bretto, Hypergraph theory. Technical ReportGoogle Scholar
  34. 34.
  35. 35.
  36. 36.
  37. 37.
    C.-Y. Lin, ROUGE: a package for automatic evaluation of summaries, in Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004) (Barcelona, Spain, 25–26 July 2004)Google Scholar
  38. 38.
    S.R. Balasundaram, P. Krishnaveni, Automatic text summarization by local scoring and ranking for improving coherence, in Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication, pp. 59–64 (2017)Google Scholar
  39. 39.
    B. Nithya, N. Ranjan, Potential node detection for route discovery in mobile ad hoc networks, in Proceedings of the International Conference on Innovative Computing and Communications Lecture Notes in Networks and Systems, vol. 55 (2018), pp. 377–388Google Scholar
  40. 40.
    M.P.S. Bhatia, R. Mittal, Identifying prominent authors from scientific collaboration multiplex social networks, in Proceedings of the International Conference on Innovative Computing and Communications Lecture Notes in Networks and Systems, vol. 55, 289–296 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Hemamalini Siranjeevi
    • 1
  • Swaminathan Venkatraman
    • 2
    Email author
  • Kannan Krithivasan
    • 3
  1. 1.Department of CSESrinivasa Ramanujan Centre, SASTRA Deemed UniversityKumbakonamIndia
  2. 2.Discrete Mathematics Laboratory, Department of MathematicsSrinivasa Ramanujan Centre, SASTRA Deemed UniversityKumbakonamIndia
  3. 3.TATA realty Srinivasa Ramanujan Research Chair, Department of MathematicsSASTRA Deemed UniversityThanjavurIndia

Personalised recommendations