Persian Text Summarization using Sparse Coding with Neural Text Representation

Authors

Abstract

The progress of communications over internet media such as social media and messengers has led to the production of large amount of textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is text summarization.
Summarizing textual information can extract the main content of text within a short time. In this paper, we propose an approach for extractive summarization on Persian texts by using sentences embedding and a sparse coding framework.

Most previous works focuses on text’s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the text’s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole text with least reconstruction error.

 The proposed approach is evaluated on Persian dataset Pasokh and achieved 10.02% and 8.65% improvement compared to the state-of-the-art methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the text’s sentences can lead us to better sentence summarization.

Keywords


  1. کهنسال، محمود. هشام فیلی، و سعید فرضی. 1396. سامانۀ خودکار خلاصه‌سازی با استفاده از روش تعبیۀ متن. مجموعه مقالات چهارمین همایش ملی زبانشناسی رایانشی. تهران: نشر نویسه پارسی (ص. 165-186).
  2. کهنسال، محمود. هشام فیلی، و سعید فرضی. 1396. سامانۀ خودکار خلاصه‌سازی با استفاده از روش تعبیۀ متن. مجموعه مقالات چهارمین همایش ملی زبانشناسی رایانشی. تهران: نشر نویسه پارسی (ص. 165-186).
  3. Behmadi Moghaddas, B., M. Kahani, A. Toosi, A. Pourmasoumi Hassankiadeh, & A. Estiry. 2013. Pasokh: a Standard Corpus for the Evaluation of Persian Text Summarizers 3rd International eConference on Computer. IEEE. Ferdowsi University of Mashhad. Mashhad, Iran. [DOI:10.1109/ICCKE.2013.6682873]
  4. Behmadi Moghaddas, B., M. Kahani, A. Toosi, A. Pourmasoumi Hassankiadeh, & A. Estiry. 2013. Pasokh: a Standard Corpus for the Evaluation of Persian Text Summarizers 3rd International eConference on Computer. IEEE. Ferdowsi University of Mashhad. Mashhad, Iran. [DOI:10.1109/ICCKE.2013.6682873]
  5. Gholamrezazadeh, S., M. AminiSalehi, & B. Gholamzadeh. 2009. A Comprehensive Survey on Text Summarization Systems. 2nd International Conference on Computer Science and its Applications. IEEE. Jeju, Korea (South). [DOI:10.1109/CSA.2009.5404226]
  6. Gholamrezazadeh, S., M. AminiSalehi, & B. Gholamzadeh. 2009. A Comprehensive Survey on Text Summarization Systems. 2nd International Conference on Computer Science and its Applications. IEEE. Jeju, Korea (South). [DOI:10.1109/CSA.2009.5404226]
  7. Hassel, M & N. Mazdak. 2004. FarsiSum: a Persian text summarizer. Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages. Geneva, Switzerland. [DOI:10.3115/1621804.1621826]
  8. Hassel, M & N. Mazdak. 2004. FarsiSum: a Persian text summarizer. Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages. Geneva, Switzerland. [DOI:10.3115/1621804.1621826]
  9. Honarpisheh, M., G. Ghassem-Sani & G. Mirroshandel. 2008. A multi-document multi-lingual automatic summarization system. Proceedings of the Third International Joint Conference on Natural Language Processing. Hyderabad, India.
  10. Honarpisheh, M., G. Ghassem-Sani & G. Mirroshandel. 2008. A multi-document multi-lingual automatic summarization system. Proceedings of the Third International Joint Conference on Natural Language Processing. Hyderabad, India.
  11. Hosseinikhah, T., A. Ahmadi A & A. Mohebi. 2018. A new Persian Text Summarization Approach based on Natural Language Processing and Graph Similarity. Iranian Journal of Information Processing and Management 33 (2): 885-914.
  12. Hosseinikhah, T., A. Ahmadi A & A. Mohebi. 2018. A new Persian Text Summarization Approach based on Natural Language Processing and Graph Similarity. Iranian Journal of Information Processing and Management 33 (2): 885-914.
  13. Karimi, Z & M. Shamsfard. 2006. Summarization of Persian texts. Proceedings of 11th International CSI computer Conference. Tehran, Iran.
  14. Karimi, Z & M. Shamsfard. 2006. Summarization of Persian texts. Proceedings of 11th International CSI computer Conference. Tehran, Iran.
  15. Khademi, M., M. Fakhredanesh, & M. Hoseini. 2017. Conceptual Text Summarizer: a new model in continuous vector space. . Journal of Information Systems and Telecommunications 7 (1): 23-33.
  16. Khademi, M., M. Fakhredanesh, & M. Hoseini. 2017. Conceptual Text Summarizer: a new model in continuous vector space. . Journal of Information Systems and Telecommunications 7 (1): 23-33.
  17. Kiyoumarsi, F. & F. Rahimi Esfahani. 2011. Optimizing Persian Text Summarization Based on Fuzzy Logic Organization. International Conference on Intelligent Building and Management. Sydney, Australia.
  18. Kiyoumarsi, F. & F. Rahimi Esfahani. 2011. Optimizing Persian Text Summarization Based on Fuzzy Logic Organization. International Conference on Intelligent Building and Management. Sydney, Australia.
  19. Lin, C.-Y. 2004. Rouge: a package for automatic evaluation of summaries. Text Summarization Branches Out. Association for Computational Linguistics. Barcelona, Spain. 84-81.
  20. Lin, C.-Y. 2004. Rouge: a package for automatic evaluation of summaries. Text Summarization Branches Out. Association for Computational Linguistics. Barcelona, Spain. 84-81.
  21. Liu, H., H. Yu, & Z-H Deng. 2015. Multi-Document Summarization Based on Two-Level Sparse Representation Model. Twenty-ninth AAAI conference on artificial intelligence. Austin, Texas, USA.
  22. Liu, H., H. Yu, & Z-H Deng. 2015. Multi-Document Summarization Based on Two-Level Sparse Representation Model. Twenty-ninth AAAI conference on artificial intelligence. Austin, Texas, USA.
  23. Mao, X., H. Yang, S. Huang, Y. Liu. & R. Li. 2019. Extractive summarization using supervised and unsupervised learing. Expert Systems with Applications 133: 173-181. [DOI:10.1016/j.eswa.2019.05.011]
  24. Mao, X., H. Yang, S. Huang, Y. Liu. & R. Li. 2019. Extractive summarization using supervised and unsupervised learing. Expert Systems with Applications 133: 173-181. [DOI:10.1016/j.eswa.2019.05.011]
  25. Masoumi, S., M-R Feizi-Derakhshi, & R. Tabatabaei.2014. TabSum- a new Persian text summarizer.. Journal of mathematics and computer science 11 (4): 330-342. [DOI:10.22436/jmcs.011.04.08]
  26. Masoumi, S., M-R Feizi-Derakhshi, & R. Tabatabaei.2014. TabSum- a new Persian text summarizer.. Journal of mathematics and computer science 11 (4): 330-342. [DOI:10.22436/jmcs.011.04.08]
  27. Mikolov, T., I. Sutskever, K. Chen, G. Corrado, & J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 (NIPS'13). Curran Associates Inc., Red Hook, NY, USA. 3111-3119.
  28. Mikolov, T., I. Sutskever, K. Chen, G. Corrado, & J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 (NIPS'13). Curran Associates Inc., Red Hook, NY, USA. 3111-3119.
  29. Parvandeh, S., S. Lahiri, & F. Boroumand. 2016. PerSum: Novel Systems for Document Summarization in Persian. International Journal of Asian Language Processing 26 (2): 67-108.
  30. Parvandeh, S., S. Lahiri, & F. Boroumand. 2016. PerSum: Novel Systems for Document Summarization in Persian. International Journal of Asian Language Processing 26 (2): 67-108.
  31. Pour-masoomi, A., M. Kahani, S. A. Toosi, & A. Estiri. 2014. Ijaz: an Operational system for single-document summarization of Persian news texts. Signal and Data Processing 11 (1): 33-48.
  32. Pour-masoomi, A., M. Kahani, S. A. Toosi, & A. Estiri. 2014. Ijaz: an Operational system for single-document summarization of Persian news texts. Signal and Data Processing 11 (1): 33-48.
  33. Rohanian, M. 2017. Multi-Document Summarization of Persian Text Using Paragraph Vectors. Proceedings of the Student Research Workshop associated with RANLP. Varna, Bulgaria. [DOI:10.26615/issn.1314-9156.2017_005]
  34. Rohanian, M. 2017. Multi-Document Summarization of Persian Text Using Paragraph Vectors. Proceedings of the Student Research Workshop associated with RANLP. Varna, Bulgaria. [DOI:10.26615/issn.1314-9156.2017_005]
  35. Shakeri, H., S. Gholamrezazadeh, M. Salehi, & F. Ghadamyari. 2012. A New Graph-Based Algorithm for Persian Text Summarization. Computer science and convergence . Dordrecht: Springer. [DOI:10.1007/978-94-007-2792-2_3]
  36. Shakeri, H., S. Gholamrezazadeh, M. Salehi, & F. Ghadamyari. 2012. A New Graph-Based Algorithm for Persian Text Summarization. Computer science and convergence . Dordrecht: Springer. [DOI:10.1007/978-94-007-2792-2_3]
  37. Shamsfard, M., T. Akhavan, & M. Erfani Jourabchi. 2009. Parsumist: a Persian text summarizer. International Conference on Natural Language Processing and Knowledge Engineering. IEEE. Dalian, China. [DOI:10.1109/NLPKE.2009.5313844]
  38. Shamsfard, M., T. Akhavan, & M. Erfani Jourabchi. 2009. Parsumist: a Persian text summarizer. International Conference on Natural Language Processing and Knowledge Engineering. IEEE. Dalian, China. [DOI:10.1109/NLPKE.2009.5313844]
  39. Soltani, M., J. Nasiri, & E. Asgarian. 2018. An Automatic Persian Text Summarization System Based on Linguistic Features and Regression. Iranian Journal of Information Processing and Management 33 (4): 1809-1828.
  40. Soltani, M., J. Nasiri, & E. Asgarian. 2018. An Automatic Persian Text Summarization System Based on Linguistic Features and Regression. Iranian Journal of Information Processing and Management 33 (4): 1809-1828.
  41. Tofighy, M., O. Kashefi, A. Zamanifar, & H. Haj Seyyed Javadi. 2011. Persian Text Summarization Using Fractal Theory. Informatics Engineering and Information Science. Berlin Heidelberg. Berlin, Heidelberg: Springer 651-662. [DOI:10.1007/978-3-642-25453-6_55]
  42. Tofighy, M., O. Kashefi, A. Zamanifar, & H. Haj Seyyed Javadi. 2011. Persian Text Summarization Using Fractal Theory. Informatics Engineering and Information Science. Berlin Heidelberg. Berlin, Heidelberg: Springer 651-662. [DOI:10.1007/978-3-642-25453-6_55]
  43. Tofighy, M., R. G. Raj, & H. Haj Seyyed Javadi. 2013. APH Techniques for Persian Text Summarization. Malaysian Journal of Computer Science 26 (1): 1-8.
  44. Tofighy, M., R. G. Raj, & H. Haj Seyyed Javadi. 2013. APH Techniques for Persian Text Summarization. Malaysian Journal of Computer Science 26 (1): 1-8.