International Journal of Parallel Programming

, Volume 46, Issue 5, pp 992–1016 | Cite as

Abstractive Text Summarization based on Improved Semantic Graph Approach

  • Atif KhanEmail author
  • Naomie Salim
  • Haleem Farman
  • Murad Khan
  • Bilal Jan
  • Awais Ahmad
  • Imran Ahmed
  • Anand Paul
Part of the following topical collections:
  1. Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm


The goal of abstractive summarization of multi-documents is to automatically produce a condensed version of the document text and maintain the significant information. Most of the graph-based extractive methods represent sentence as bag of words and utilize content similarity measure, which might fail to detect semantically equivalent redundant sentences. On other hand, graph based abstractive method depends on domain expert to build a semantic graph from manually created ontology, which requires time and effort. This work presents a semantic graph approach with improved ranking algorithm for abstractive summarization of multi-documents. The semantic graph is built from the source documents in a manner that the graph nodes denote the predicate argument structures (PASs)—the semantic structure of sentence, which is automatically identified by using semantic role labeling; while graph edges represent similarity weight, which is computed from PASs semantic similarity. In order to reflect the impact of both document and document set on PASs, the edge of semantic graph is further augmented with PAS-to-document and PAS-to-document set relationships. The important graph nodes (PASs) are ranked using the improved graph ranking algorithm. The redundant PASs are reduced by using maximal marginal relevance for re-ranking the PASs and finally summary sentences are generated from the top ranked PASs using language generation. Experiment of this research is accomplished using DUC-2002, a standard dataset for document summarization. Experimental findings signify that the proposed approach shows superior performance than other summarization approaches.


Multi-document abstractive summarization Semantic graph Semantic role labeling (SRL) Semantic similarity measure Graph based ranking algorithm 



This research is supported by Higher Education Commission (HEC), Pakistan and Department of Computer Science, Islamia College, Peshawar, Pakistan. This research is also supported by Next-Generation Information Computing Development Program through the National Research Foundation (NRF) funded by the Korean Government (MSIT) (2017M3C4A7066010). This work is also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (NRF2016R1A2A1A05005459).


  1. 1.
    Fattah, M.A., Ren, F.: GA, MR, FFNN, PNN and GMM based models for automatic text summarization. Comput. Speech Lang. 23(1), 126–144 (2009)CrossRefGoogle Scholar
  2. 2.
    Barzilay, R., McKeown, K.R.: Sentence fusion for multidocument news summarization. Comput. Linguist. 31(3), 297–328 (2005)CrossRefGoogle Scholar
  3. 3.
    Das, D., Martins, A.F.: A survey on automatic text summarization. Lit. Surv. Lang. Stat. II course at CMU 4, 192–195 (2007)Google Scholar
  4. 4.
    Ye, S., Chua, T.-S., Kan, M.-Y., Qiu, L.: Document concept lattice for text understanding and summarization. Inf. Process. Manag. 43(6), 1643–1662 (2007)CrossRefGoogle Scholar
  5. 5.
    Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  6. 6.
    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, Washington, USA, 9–13 July 1995, pp. 68-73. ACM (1995)Google Scholar
  7. 7.
    Knight, K., Marcu, D.: Statistics-based summarization-step one: Sentence compression. In: Proceedings of the National Conference on Artificial Intelligence 2000, pp. 703–710. AAAI Press, Menlo Park (1999)Google Scholar
  8. 8.
    Larsen, B.: A trainable summarizer with knowledge acquired from robust NLP techniques. Adv. Autom. Text Summ. 71 (1999)Google Scholar
  9. 9.
    Fattah, M.A.: A hybrid machine learning model for multi-document summarization. Appl. Intell. 40(4), 592–600 (2014)CrossRefGoogle Scholar
  10. 10.
    Erkan, G., Radev, D.R.: LexPageRank: prestige in multi-document text summarization. In: EMNLP 2004, pp. 365–371 (2004)Google Scholar
  11. 11.
    Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. (JAIR) 22(1), 457–479 (2004)CrossRefGoogle Scholar
  12. 12.
    Mihalcea, R., Tarau, P.: A language independent algorithm for single and multiple document summarization (2005)Google Scholar
  13. 13.
    Wan, X., Yang, J.: Improved affinity graph based multi-document summarization. In: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, New York City, USA, June 2006, pp. 181–184. ACL (2006)Google Scholar
  14. 14.
    Barzilay, R., McKeown, K.R., Elhadad, M.: Information fusion in the context of multi-document summarization. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, Maryland, 20–26 June 1999, pp. 550–557. ACL (1999)Google Scholar
  15. 15.
    Tanaka, H., Kinoshita, A., Kobayakawa, T., Kumano, T., Kato, N.: Syntax-driven sentence revision for broadcast news summarization. In: Proceedings of the 2009 Workshop on Language Generation and Summarisation, Suntec, Singapore, 6 August 2009, pp. 39–47. ACL (2009)Google Scholar
  16. 16.
    Genest, P.-E., Lapalme, G.: Framework for abstractive summarization using text-to-text generation. In: Proceedings of the workshop on monolingual text-to-text generation, Oregon, USA, 24 June 2011, pp. 64–73. ACL (2011)Google Scholar
  17. 17.
    Harabagiu, S.M., Lacatusu, F.: Generating single and multi-document summaries with gistexter. In: Document Understanding Conferences, Pennsylvania, USA, 11–12 July 2002, pp. 40–45. NIST (2002)Google Scholar
  18. 18.
    Genest, P.-E., Lapalme, G.: Fully abstractive approach to guided summarization. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Jeju Island, Korea, 8–14 July 2012, pp. 354–358. ACL (2012)Google Scholar
  19. 19.
    Lee, C.-S., Jian, Z.-W., Huang, L.-K.: A fuzzy ontology and its application to news summarization. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 35(5), 859–880 (2005)CrossRefGoogle Scholar
  20. 20.
    Greenbacker, C.F.: Towards a framework for abstractive summarization of multimodal documents. ACL HLT 2011, 75 (2011)Google Scholar
  21. 21.
    Moawad, I.F., Aref, M.: Semantic graph reduction approach for abstractive text summarization. In: 7th International Conference on Computer Engineering andSystems (ICCES), 2012, pp. 132–138. IEEE (2012)Google Scholar
  22. 22.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. (1999)Google Scholar
  23. 23.
    Mani, I., Bloedorn, E.: Summarizing similarities and differences among related documents. Inf. Retr. 1(1–2), 35–67 (1999)CrossRefGoogle Scholar
  24. 24.
    Zhang, J., Sun, L., Zhou, Q.: A cue-based hub-authority approach for multi-document text summarization. In: Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE’05, 2005, pp. 642–645. IEEE (2005)Google Scholar
  25. 25.
    Wei, F., Li, W., Lu, Q., He, Y.: A document-sensitive graph model for multi-document summarization. Knowl. Inf. Syst. 22(2), 245–259 (2010)CrossRefGoogle Scholar
  26. 26.
    Ge, S.S., Zhang, Z., He, H.: Weighted graph model based sentence clustering and ranking for document summarization. In: 4th International Conference on Interaction Sciences (ICIS), 2011, pp. 90–95. IEEE (2011)Google Scholar
  27. 27.
    Nguyen-Hoang, T.-A., Nguyen, K., Tran, Q.-V.: TSGVi: a graph-based summarization system for Vietnamese documents. J. Ambient Intell. Humaniz. Comput. 3(4), 305–313 (2012)CrossRefGoogle Scholar
  28. 28.
    Cheung, J.C.K., Penn, G.: Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain. In: ACL (1), pp. 1233–1242 (2013)Google Scholar
  29. 29.
    Glavaš, G., Šnajder, J.: Event graphs for information retrieval and multi-document summarization. Expert Syst. Appl. 41(15), 6904–6916 (2014)CrossRefGoogle Scholar
  30. 30.
    Liu, F., Flanigan, J., Thomson, S., Sadeh, N., Smith, N.A.: Toward abstractive summarization using semantic representations. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, 1–5 June 2015, pp. 1077–1086. ACL (2015)Google Scholar
  31. 31.
    Bing, L., Li, P., Liao, Y., Lam, W., Guo, W., Passonneau, R.J.: Abstractive multi-document summarization via phrase selection and merging. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, 26–31 July 2015, pp. 1587–1597. ACL (2015)Google Scholar
  32. 32.
    Boudin, F., Mougard, H., Favre, B.: Concept-based summarization using integer linear programming: from concept pruning to multiple optimal solutions. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) 2015, Lisbon, Portugal, 17–21 September 2015, pp. 1914–1918. ACL (2015)Google Scholar
  33. 33.
    Belkebir, R., Guessoum, A.: Concept generalization and fusion for abstractive sentence generation. Expert Syst. Appl. 53, 43–56 (2016)CrossRefGoogle Scholar
  34. 34.
    Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1–66 (2017)CrossRefGoogle Scholar
  35. 35.
    Cuomo, S., De Michele, P., Piccialli, F., Galletti, A., Jung, J.E.: IoT-based collaborative reputation system for associating visitors and artworks in a cultural scenario. Expert Syst. Appl. 79, 101–111 (2017)CrossRefGoogle Scholar
  36. 36.
    Farina, R., Cuomo, S., De Michele, P., Piccialli, F.: A smart GPU implementation of an elliptic kernel for an ocean global circulation model. Appl. Math. Sci. 7(61–64), 3007–3021 (2013)Google Scholar
  37. 37.
    Piccialli, F., Cuomo, S., De Michele, P.: A regularized mri image reconstruction based on hessian penalty term on CPU/GPU systems. Proc. Comput. Sci. 18, 2643–2646 (2013)CrossRefGoogle Scholar
  38. 38.
    Chianese, A., Marulli, F., Moscato, V., Piccialli, F.: A “smart” multimedia guide for indoor contextual navigation in cultural heritage applications. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2013, pp. 1–6. IEEE (2013)Google Scholar
  39. 39.
    Chianese, A., Piccialli, F.: SmaCH: a framework for smart cultural heritage spaces. In: 10th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), 2014, pp. 477–484. IEEE (2014)Google Scholar
  40. 40.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)zbMATHGoogle Scholar
  41. 41.
    Barnickel, T., Weston, J., Collobert, R., Mewes, H.-W., Stümpflen, V.: Large scale application of neural network based semantic role labeling for automated relation extraction from biomedical texts. PLoS ONE 4(7), e6393 (2009)CrossRefGoogle Scholar
  42. 42.
    Gatt, A., Reiter, E.: SimpleNLG: a realisation engine for practical applications. In: Proceedings of the 12th European Workshop on Natural Language Generation, Athens, Greece, 30–31 March 2009, pp. 90–93. ACL (2009)Google Scholar
  43. 43.
    Porter, M.F.: Snowball: a language for stemming algorithms (2001)Google Scholar
  44. 44.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. Preprint arXiv:cmp-lg/9709008 (1997)
  45. 45.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  46. 46.
    Suanmali, L., Salim, N., Binwahlan, M.S.: Fuzzy logic based method for improving text summarization. Int. J. Comput. Sci. Inf. Secur. 2(1), 65–70 (2009)Google Scholar
  47. 47.
    Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27(6), 17–26 (1994)CrossRefGoogle Scholar
  48. 48.
    Panda, S., Padhy, N.P.: Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Appl. Soft Comput. 8(4), 1418–1427 (2008)CrossRefGoogle Scholar
  49. 49.
    Lin, C.-Y.: Rouge: a package for automatic evaluation of summaries. In: Proceedings of the ACL-04 workshop ontext summarization branches out, Barcelona, Spain, 25–26 July 2004, pp. 74–81. ACL (2004)Google Scholar
  50. 50.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998)CrossRefGoogle Scholar
  51. 51.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  52. 52.
    Mihalcea, R., Tarau, P.: A language independent algorithm for single and multiple document summarization. (2005)
  53. 53.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, 24–28 August 1998, pp. 335–336. ACM (1998)Google Scholar
  54. 54.
    Jaccard, Paul: Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)Google Scholar
  55. 55.
    Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI 2006, pp. 775–780 (2006)Google Scholar
  56. 56.
    Nenkova, A., Passonneau, R.: Evaluating content selection in summarization: the pyramid method. In: 2004. NAACL-HLT (2004)Google Scholar
  57. 57.
    Over, P., Liggett, W.: Introduction to DUC: an intrinsic evaluation of generic news text summarization systems. (2002)

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

Authors and Affiliations

  1. 1.Department of Computer ScienceIslamia CollegePeshawarPakistan
  2. 2.Faculty of ComputingUniversiti Teknologi MalaysiaJohorMalaysia
  3. 3.Department of Computer and ITSarhad University of Science and ITPeshawarPakistan
  4. 4.Department of Computer ScienceFATA UniversityDara Adam KhelPakistan
  5. 5.Department of Information and Communication EngineeringYeungnam UniversityGyeongsanRepublic of Korea
  6. 6.Institute of Management SciencePeshawarPakistan
  7. 7.School of Computer Science and EngineeringKyugpook National UniversityDaeguRepublic of Korea

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