Hindi Story Heading Generation Using Proverb Identification

  • Leena Jain
  • Prateek AgrawalEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


This paper explains 02 methods to generate the headings or titles of Hindi stories. We have developed a title generation tool that takes a story (without any length limit) as input and suggests some titles based on (i) keyword matching and (ii) proverb sensing. In keyword matching approach, it deals with database which includes keywords associated with each proverb. The corpora contain 35135 Hindi words which are classified separately among Nouns, Adjectives, Verbs, Adverbs, and Quantifiers etc. Both algorithms for generating the titles are described in this paper. RD Parts Of Speech Tagger (an open source POS tagger) has been referred and some tagged word enhancements have been done in the POS tagger corpora to improve the efficiency. Our proposed tool was tested on randomly selected 40 common Hindi short stories and the system produces more than 90% relevant (The results were verified and validated by 02 School Hindi Teachers having more than 10 years experience (St. Soldiers Public School, Jalandhar, Punjab, India)) titles using proverb sensing method. This application can be recommended as an informative tool for school going students as well as for school Hindi teachers as an effective pedagogy tool for teaching and learning. It can also be helpful to Hindi newspaper editors, blog writers and technical writers in finding the tentative titles for their articles.


Transliteration Heading generation Language teaching tool Natural language processing 

Supplementary material


  1. 1.
  2. 2.
  3. 3.
    Dorr, B., Zajic, D., Schwartz, R.: Cross-language headline generation for Hindi. ACM Trans. Asian Lang. Inf. Process. 2(3), 270–289 (2003)CrossRefGoogle Scholar
  4. 4.
    Sethi, N., Agrawal, P., Madaan, V., Singh, S.K.: A novel approach to paraphrase hindi sentences using natural language processing. Indian J. Sci. Technol. 9(28), 1–6 (2016)Google Scholar
  5. 5.
    Jain, L., Agrawal, P.: Text independent root word identification in Hindi language using natural language processing. Int. J. Adv. Intell. Paradigm 7(3/4), 240–249 (2015)CrossRefGoogle Scholar
  6. 6.
    Jain, L., Agrawal, P.: English to sanskrit transliteration: an effective approach to design natural language translation tool. Int. J. Adv. Res. Comput. Sci. 8(1), 1–4 (2017)Google Scholar
  7. 7.
    Sethi, N., Agrawal, P., Madaan, V., Singh, S.K., Kakran, A.: Automated title generation in english language using NLP. Int. J. Control Theory Appl. 9(11), 5159–5168 (2016)Google Scholar
  8. 8.
    Madaan, V., Agrawal, P., Sethi, N., Kumar, V., Singh, S.K.: A novel approach to paraphrase english sentences using natural language processing. Int. J. Control Theory Appl. 9(11), 5119–5128 (2016)Google Scholar
  9. 9.
    Yousif, J.H.: Natural language processing based soft computing techniques. Int. J. Comput. Appl. 77(8), 43–50 (2013)Google Scholar
  10. 10.
    Haque, R., Dandapat, S., Srivastava, A.K., Naskar, S.K., Way, A.: English-Hindi transliteration using context-informed PB-SMT: the DCU system for NEWS 2009. In: Proceedings of the 2009 Named Entities Workshop Shared Task on Transliteration (NEWS 2009), pp. 104–107 (2009)Google Scholar
  11. 11.
  12. 12.
    Patterson, D.W.: Introduction to AI and Expert Systems, 2nd edn. Prentice Hall, Upper Saddle River (2012)Google Scholar
  13. 13.
    Rich, E., Knight, K., Nair, S.V.: Artificial Intelligence, 3rd edn. Tata McGraw Hill, New York (2009)Google Scholar
  14. 14.
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.IKG-Punjab Technical UniversityKapurthalaIndia
  2. 2.Lovely Professional UniversityPhagwaraIndia

Personalised recommendations