English to Hindi Cross-Lingual Text Summarizer using TextRank Algorithm


Sunita Rawat
Kavita Kalambe
Sagarika Jaywant
Lakshita Werulkar
Mukul Barbate
Tarrun Jaiswalt


Cross-Lingual Summarizer develops a gist of the extract written in English in the National Language of India Hindi. This helps non-anglophonic people to understand what the text says in Hindi. The extractive method of summarization is being used in this paper for summarizing the article. The summary generated in English is then translated into Hindi and made available for Hindi Readers. The Hindi readers get the heart of the article they want to read. Due to the Internet’s explosive growth, access to a vast amount of information is now efficient but getting harder and harder. An approach to text extraction summarization that captures the aboutness of the text document was discussed in this paper. One of the many uses for natural language processing (NLP) that significantly affects our daily lives is text summarization. Who has the time to read through complete articles, documents, or books to determine whether they are helpful with the expansion of digital media and the profusion of articles published? The technique was created using TextRank, which was determined using the idea of PageRank established for each page on a website. The presented approach builds a graph with sentences as nodes and the weight of the edge connecting two sentences as its nodes. Modified inverse sentence-cosine frequency similarity gives different words in a sentence different weights. The success of the procedure is demonstrated by the performance evaluation that supported the summary technique.


How to Cite
Rawat, S., Kalambe, K., Jaywant, S., Werulkar, L., Barbate, M., & Jaiswalt, T. (2023). English to Hindi Cross-Lingual Text Summarizer using TextRank Algorithm. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1025


  1. Andhale, N. and Bewoor, L. 2016. An overview of text summarization techniques. 1–7. DOI: https://doi.org/10.1109/ICCUBEA.2016.7860024
  2. Dhariya, O., Malviya, S., and Tiwary, U. S. 2017. A hybrid approach for hindi-english machine translation. 2017 International Conference on Information Networking (ICOIN), 389–394. DOI: https://doi.org/10.1109/ICOIN.2017.7899465
  3. Hingu, D., Shah, D., and Udmale, S. S. 2015. Automatic text summarization of wikipedia articles. 2015 International Conference on Communication, Information & Computing Technology (ICCICT), 1–4. DOI: https://doi.org/10.1109/ICCICT.2015.7045732
  4. Mihalcea, R. and Tarau, P. 2004. Textrank: Bringing order into text.
  5. Mikolov, T., Chen, K., Corrado, G., and Dean, J. 2013. Efficient estimation of word representations in vector space.
  6. Patel, M. and Shah, A. 2017. An application of mdl principle for indian resource poor language. 8, 185.
  7. Patil, A., Dalmia, S., Ansari, S., Aul, T., and Bhatnagar, V. 2014. Automatic text summarizer. 1530–1534. DOI: https://doi.org/10.1109/ICACCI.2014.6968629
  8. Raju, T. S. R. and Allarpu, B. 2017. Cross-language document summarization via extraction and ranking of multiple summaries. International Research Journal of Engineering and Technology 04, 1777–1779.
  9. Rawat, S. 2015a. A comparative study on different approaches to word sense disambiguation, In Proc. of the National Conference on Research in Cloud and Cyber Security (NCRCCS - 2015).
  10. Rawat, S. 2015b. A review on word sense disambiguation. International Journal of Innovative Research in Computer Communications Engineering.
  11. Rawat, S. 2015c. Word sense disambiguation and classification algorithms: A review. International Journal of Computer Science and Applications.
  12. Rawat, S. 2016a. An approach for efficient machine translation using translation memory, International Conference SMARTCOM 2016, Jaipur DOI: https://doi.org/10.1007/978-981-10-3433-6_34
  13. Rawat, S. 2016b. Comparative survey of document analysis and categorization techniques, International Conference On Recent Advances in Computer Science, E-Learning, Information & Communication Technology (CSIT– 2016)
  14. Rawat, S. 2017. An approach for improving accuracy of machine translation using wsd and giza. International Journal of Computer Sciences and Engineering. DOI: https://doi.org/10.26438/ijcse/v5i10.256259
  15. Rawat, S. 2019. Supervised word sense disambiguation using decision tree. International Journal
  16. of Recent Technology and Engineering (IJRTE), 4043–4047.
  17. Rawat, S. 2022. A method to integrate word sense disambiguation and translation memory for english to hindi machine translation system. Computer Assisted Methods in Engineering and Science (CAMES).
  18. Saini, S. and Sahula, V. 2018. Neural machine translation for english to hindi. DOI: https://doi.org/10.1109/INFRKM.2018.8464781
  19. Wan, X., Li, H., and Xiao, J. 2010. Cross-language document summarization based on machine translation quality prediction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Uppsala, Sweden, 917–926.
  20. Wan, X., Luo, F., Sun, X., Huang, S., and Yao, J.-g. 2019. Cross-language document summarization via extraction and ranking of multiple summaries. Knowledge and Information Systems 58. DOI: https://doi.org/10.1007/s10115-018-1152-7