Question Answer Based Chart Summarization

  • Aditi Deshpande
  • Namrata MahenderEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


To summarize documents worths to summationof the main points. A summarization is this kind of summing up. Elementary school book reports are big on summarization. To provide a comprehensible declaration of the significant points is nothing but summarization. In current years, natural language processing (NLP) has stimulated to statistica1l base. Many tribulations in NLP, e.g., parsing, word sense disambiguation, and involuntary paraphrasing. In recent times, robust graph-based methods for NLP is also a lot of scope, e.g., in clustering of words and attachments of prepositional phrase. In proposed paper, we will take in account of graph-based summarization techniques, approaches used for that etc. We will talk about how arbitrary traversing on images of graphs can help in making of question answer based summarization. In current exploration work, question answer based graph summarization system for Bar Graph is shown. The extraction procedure is completely computerized using image processing and text recognition methods. The extracted information can be used to improve the indexing component for bar charts and get better exploration results. After generating questions, questions are rank the according to frequency or priority and answer of the ranked question is summary of given input.


Extraction of chart data Question answer generation system 


  1. 1.
    Cafarella, M.J., Chen, S.Z., Adar, E.: Searching for statistical diagrams. Front. Eng. Natl. Acad. Eng. 69-78 (2011)Google Scholar
  2. 2.
    Adar, E., Chen, Z., Cafarella, M.: Diagramflyer: a search engine for data-driven diagrams. In: Proceedings of the 24th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp. 183–186 (2015)Google Scholar
  3. 3.
    Chester, D., Elzer, S.: Getting computers to see information graphics so users do not have to. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 660–668. Springer, Heidelberg (2005). Scholar
  4. 4.
    Kasturi, R., Fletcher, L.A.: A robust algorithm for text string separation from mixed text/graphics images. IEEE Trans. Pattern Anal. Mach. Intell. 10, 910–918 (1988)CrossRefGoogle Scholar
  5. 5.
    Huang, W., Tan, C.L., Leow, W.K.: Model-based chart image recognition. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 87–99. Springer, Heidelberg (2004). Scholar
  6. 6.
    Leow, W.K., Huang, W., Tan, C.L.: Associating text and graphics for scientific chart understanding. In: 2005 Eighth International Conference on Document Analysis and Recognition, Proceedings. IEEE, pp. 580–584 (2005)Google Scholar
  7. 7.
    Giles, C.L., Liu, Y., Mitra, P., Bai, K.: Automatic extraction of table metadata from digital documents. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 339-340. ACM (2006)Google Scholar
  8. 8.
    Atlas. (2015)
  9. 9.
    Fei-Fei, L., Agrawala, M., Savva, M., Kong, N., Chhajta, A., Heer, J.: Revision: automated classification, analysis and redesign of chart images. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 393–402. ACM (2011)Google Scholar
  10. 10.
    Fomina, Y., Vassilieva, N.: Text detection in chart images. Pattern Recognit. Image Anal. 23, 139–144 (2013)CrossRefGoogle Scholar
  11. 11.
    Al-Zaidy, R.A., Giles, C.L.: Automatic extraction of data from bar charts. In: Proceedings of the International Conference on Knowledge Capture, K-CAP, pp. 30:1–30:4 (2015)Google Scholar
  12. 12.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846, January 1998Google Scholar
  13. 13.
    Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press (1983)Google Scholar
  14. 14.
    Yang, L., Huang, W., Tan, C.L.: Semi-automatic ground truth generation for chart image recognition. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 324–335. Springer, Heidelberg (2006). Scholar
  15. 15.
    Zhou, Y.P., Tan, C.L.: Hough technique for bar charts detection and recognition in document images. In: International Conference on Image Processing, pp. 605–608, September 2000Google Scholar
  16. 16.
    Stolte, C., Tang, D., Hanrahan, P.: Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. Vis. Comput. Graph. 8(1), 52–65 (2002)CrossRefGoogle Scholar
  17. 17.
    Huang, W., Tan, C.L.: A system for understanding imaged infographics and its applications. In: Proceedings of the ACM Symposium on Document Engineering, DocEng 2007, pp. 9–18. ACM, New York (2007)Google Scholar
  18. 18.
    Liu, R., Huang, W., Tan, C.L.: Extraction of vectorized graphical information from scientific chart images. In: Document Analysis & Recognition (ICDAR), pp. 521–525 (2007)Google Scholar
  19. 19.
    Mackinlay, J.D.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110–141 (1986)CrossRefGoogle Scholar
  20. 20.
    Mackinlay, J.D., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. IEEE Trans. Vis. Comput. Graph. 13(6), 1137–1144 (2007)CrossRefGoogle Scholar
  21. 21.
    Santosh, K.C., Wendling, L.: Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9, 678–690 (2014)CrossRefGoogle Scholar
  22. 22.
    Santosh, K.C., Lamiroy, B.: Character recognition based on DTW-Radon. IAPR, International Conference on Document Analysis and Recognition (ICDAR), pp. 264–268. IEEE (2013)Google Scholar
  23. 23.
    Santosh, K.C., Aafaque, A.: Line segment-based stitched multipanel figure separation for effective biomedical CBIR. Int. J. Pattern Recognit. Artif. Intell. (IJPRAI) 31(6), 1–18 (2017)CrossRefGoogle Scholar
  24. 24.
    Ghosh, S., Lahiri, D., Bhowmik, S., Kavallieratou, E., Sarkar, R.: g-DICE: graph mining based document Information Content Exploitation. Int. J. Doc. Anal. Recognit. (IJDAR) 18(04), 337–355 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceDr. BAMU UniversityAurangabadIndia

Personalised recommendations