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A New Approach for Single Text Document Summarization

  • Chandra Shekhar Yadav
  • Aditi Sharan
  • Rakesh Kumar
  • Payal Biswas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

This paper proposes an extraction-based hybrid model for a single text document summarization. The hybrid model is depending on the linear combination of statistical measures like sentence position, TF-IDF, aggregate similarity, centroid, and sentiment analysis. Our idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message; hence, it can play vital role in text document summarization. As we know for any sentence, emotions (calling sentiments) may be negative, positive, or neutral. Sentence which has strong sentiment are more important for us which may be either negative or positive.

Keywords

Single document summarization Sentiment analysis Hybrid model 

Notes

Acknowledgments

Thanks to UGC for funding and special thanks to Iskandar Keskes (Miracl loboratory, ANLP-Research Group, Sfax-Tunisia), Ashish Kumar (SC & SS, LAB-01, JNU).

References

  1. 1.
    Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2, 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baxendale, P.B.: Machine-made index for technical literature: an experiment. IBM J. Res. Dev. 2, 354–361 (1958)CrossRefGoogle Scholar
  3. 3.
    Edmundson, H.P.: New methods in automatic extracting. J. ACM 16, 264–285 (1969)MATHCrossRefGoogle Scholar
  4. 4.
    Radev, D.R., Jing, H., Stys, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Process. Manage. 40, 919–938 (2004)MATHCrossRefGoogle Scholar
  5. 5.
    Goldstein, J., Mittal, V., Carbonell, J., Callan, J.: Creating and evaluating multi-document sentence extract summaries. In: Proceedings of the 9th International Conference Information and Knowledge Management, pp. 165–172. ACM (2000)Google Scholar
  6. 6.
    Alguliev, R.M., Aliguliyev, R.M., Hajirahimova, M.S., Mehdiyev, C.A.: MCMR: Maximum coverage and minimum redundant text summarization model. Expert Syst. Appl. 38, 14514–14522 (2011)CrossRefGoogle Scholar
  7. 7.
    Sarkar, K.: Syntactic trimming of extracted sentences for improving extractive multi document summarization. J. Comput. 2 (2010)Google Scholar
  8. 8.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st International Conference Research and Development in Information Retrieval, pp. 335–336. ACM SIGIR (1998)Google Scholar
  9. 9.
    Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Proceedings of the Text Summarization Branches Out, ACL-04 Workshop, pp. 74–81 (2004)Google Scholar
  10. 10.
    Ko, Y., Seo, J.: An effective sentence-extraction technique using contextual information and statistical approaches for text summarization. Pattern Recogn. Lett. 29, 1366–1371 (2008)CrossRefGoogle Scholar
  11. 11.
    Yeh, J.Y., Ke, H.R., Yang, W.P., Meng, I.H.: Text summarization using a trainable summarizer and latent semantic analysis. Inf. Process. Manage. 41, 75–95 (2005)CrossRefGoogle Scholar
  12. 12.
    Radev, D.R., Blair-Goldensohn, S., Zhang, Z.: Experiments in single and multi-document summarization using MEAD. In: 1st Conference Document Understanding, New Orleans, LA (2001)Google Scholar
  13. 13.
    Kim, J.H., Kim, J.H., Hwang, D.: Korean text summarization using an aggregate similarity. In: Proceedings of the 5th International Workshop on Information Retrieval with Asian languages, pp. 111–118. ACM (2000)Google Scholar
  14. 14.
    Ganesan, K., Zhai, C., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference Computational Linguistics, pp. 340–348. ACL (2010)Google Scholar
  15. 15.
    Yadav, C.S., Sharan, A., Joshi, M.L.: Semantic graph based approach for text mining. In: International Conference Challenges in Intelligent Computing Techniques, pp. 596–601. IEEE (2014)Google Scholar
  16. 16.
    Yadav, C.S., Sharan, A.: Hybrid approach for single text document summarization using statistical and sentiment features. Int. J. Inf. Retr. Res. (IJIRR), 5(4), 46–70 (2015)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Chandra Shekhar Yadav
    • 1
  • Aditi Sharan
    • 1
  • Rakesh Kumar
    • 1
  • Payal Biswas
    • 1
  1. 1.SC & SS, Jawarlal Nehru UniversityNew DelhiIndia

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