An Abstract Argumentation-Based Approach to Automatic Extractive Text Summarization

  • Stefano Ferilli
  • Andrea Pazienza
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 806)


Sentence-based extractive summarization aims at automatically generating shorter versions of texts by extracting from them the minimal set of sentences that are necessary and sufficient to cover their content. Providing effective solutions to this task would allow the users of Digital Libraries to save time in selecting documents that may be appropriate for satisfying their information needs or for supporting their decision-making tasks. This paper proposes an approach, based on abstract argumentation, to select the sentences in a text that are to be included in its summary. The proposed approach obtained interesting experimental results on the English subset of the benchmark MultiLing 2015 dataset.


Text summarization Digital libraries Abstract argumentation 



This work was partially funded by the Italian PON 2007–2013 project PON02_00563_3489339 ‘Puglia@Service’.


  1. 1.
    Banerjee, S., Mitra, P., Sugiyama, K.: Multi-document abstractive summarization using ILP based multi-sentence compression. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, pp. 1208–1214 (2015)Google Scholar
  2. 2.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: ACM SIGIR, pp. 335–336. ACM (1998)Google Scholar
  3. 3.
    Cayrol, C., Lagasquie-Schiex, M.C.: On the acceptability of arguments in bipolar argumentation frameworks. In: Godo, L. (ed.) ECSQARU 2005. LNCS (LNAI), vol. 3571, pp. 378–389. Springer, Heidelberg (2005). CrossRefGoogle Scholar
  4. 4.
    Chen, D., Manning, C.D.: A fast and accurate dependency parser using neural networks. In: EMNLP, pp. 740–750 (2014)Google Scholar
  5. 5.
    Davis, S.T., et al.: OCCAMS-an optimal combinatorial covering algorithm for multi-document summarization. In: ICDMW, pp. 454–463. IEEE (2012)Google Scholar
  6. 6.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Dunne, P.E., et al.: Weighted argument systems: Basic definitions, algorithms, and complexity results. Artif. Intell. 175(2), 457–486 (2011)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Ferilli, S., Pazienza, A., Angelastro, S., Suglia, A.: A similarity-based abstract argumentation approach to extractive text summarization. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017. LNCS, vol. 10640, pp. 87–100. Springer, Cham (2017). CrossRefGoogle Scholar
  9. 9.
    Ferreira, R., et al.: Assessing sentence scoring techniques for extractive text summarization. Expert Syst. Appl. 40(14), 5755–5764 (2013)CrossRefGoogle Scholar
  10. 10.
    Ferreira, R., et al.: A new sentence similarity assessment measure based on a three-layer sentence representation. In: DocEng, pp. 25–34. ACM (2014)Google Scholar
  11. 11.
    Giannakopoulos, G., et al.: Multiling 2015: multilingual summarization of single and multi-documents, on-line fora, and call-center conversations. In: SIGDIAL, pp. 270–274 (2015)Google Scholar
  12. 12.
    Gupta, P., et al.: Summarizing text by ranking text units according to shallow linguistic features. In: ICACT, pp. 1620–1625. IEEE (2011)Google Scholar
  13. 13.
    Lin, C.: Rouge: A package for automatic evaluation of summaries. In: ACL 2004 Workshop, vol. 8 (2004)Google Scholar
  14. 14.
    Lloret, E., Palomar, M.: Text summarisation in progress: a literature review. Artif. Intell. Rev. 37(1), 1–41 (2012)CrossRefGoogle Scholar
  15. 15.
    Manning, C.D., et al.: The stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)Google Scholar
  16. 16.
    Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. Association for Computational Linguistics (2004)Google Scholar
  17. 17.
    Miller, G.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  18. 18.
    Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 43–76. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  19. 19.
    Pazienza, A., Esposito, F., Ferilli, S.: An authority degree-based evaluation strategy for abstract argumentation frameworks. In: Proceedings of the 30th Italian Conference on Computational Logic, pp. 181–196 (2015)Google Scholar
  20. 20.
    Rotella, F., Leuzzi, F., Ferilli, S.: Learning and exploiting concept networks with conNeKTion. Appl. Intell. 42(1), 87–111 (2015)CrossRefGoogle Scholar
  21. 21.
    Shardan, R., Kulkarni, U.: Implementation and evaluation of evolutionary connectionist approaches to automated text summarization. J. Comput. Sci. 6, 1366–1376 (2010)CrossRefGoogle Scholar
  22. 22.
    Umam, K., et al.: Coverage, diversity, and coherence optimization for multi-document summarization. Jurnal Ilmu Komputer dan Informasi 8(1), 1–10 (2015)CrossRefGoogle Scholar
  23. 23.
    Vasilescu, F., Langlais, P., Lapalme, G.: Evaluating variants of the lesk approach for disambiguating words. In: LREC (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità di BariBariItaly

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