CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Literature Review on Extractive Text Summarization Approaches

by Saiyed Saziyabegum, Priti S. Sajja
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 12
Year of Publication: 2016
Authors: Saiyed Saziyabegum, Priti S. Sajja
10.5120/ijca2016912574

Saiyed Saziyabegum, Priti S. Sajja . Literature Review on Extractive Text Summarization Approaches. International Journal of Computer Applications. 156, 12 ( Dec 2016), 28-36. DOI=10.5120/ijca2016912574

@article{ 10.5120/ijca2016912574,
author = { Saiyed Saziyabegum, Priti S. Sajja },
title = { Literature Review on Extractive Text Summarization Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 12 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 28-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number12/26762-2016912574/ },
doi = { 10.5120/ijca2016912574 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:26.313862+05:30
%A Saiyed Saziyabegum
%A Priti S. Sajja
%T Literature Review on Extractive Text Summarization Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 12
%P 28-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There is plenty of information available on internet. Important information can be considered by creating summary from available information. Manual creation of summary is complicated task. Therefore research community is developing new approaches to for automatic text summarization. Automatic text summarization system creates summary. Summary is shorter text that covers important information from original document. Summary can be created using extractive and abstractive methods. Abstractive methods are requires deep understanding of text. After understanding, it represents text into new simple notions in shorter form. Extractive approach uses linguistic and statistical approach for selection of sentences for summary. This paper presents an ample survey of recent text summarization extractive approaches developed in last few decades. Summary evaluation is also covered briefly in this paper. Finally this paper ends with conclusion of future research needed.

References
  1. Jezek, K., & Steinberger, J. (2008). Automatic text summarization. InZnalosti (pp. 1-12).
  2. Kyoomarsi, F., Khosravi, H., Eslami, E., Dehkordy, P. K., & Tajoddin, A. (2008, May). Optimizing Text Summarization Based on Fuzzy Logic. InComputer and Information Science, 2008. ICIS 08. Seventh IEEE/ACIS International Conference on (pp. 347-352). IEEE.
  3. Baxendale, P. B. (1958). Machine-made index for technical literature: an experiment. IBM Journal of Research and Development, 2(4), 354-361.
  4. Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2005). Tapping into the power of text mining.
  5. Chen, F., Han, K., & Chen, G. (2002, October). An approach to sentence-selection-based text summarization. In TENCON'02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering (Vol. 1, pp. 489-493). IEEE.
  6. Fattah, M. A., & Ren, F. (2008). Automatic text summarization. World Academy of Science, Engineering and Technology, 37, 2008.
  7. Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of research and development, 2(2), 159-165.
  8. Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM (JACM), 16(2), 264-285.
  9. Kupiec, J., Pedersen, J., & Chen, F. (1995, July). A trainable document summarizer. In Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 68-73). ACM.
  10. Brandow, R., Mitze, K., & Rau, L. F. (1995). Automatic condensation of electronic publications by sentence selection. Information Processing & Management, 31(5), 675-685.
  11. Okurowski, M. G. J., Aone, C., & Larsen, I. (1999). B. A trainable summarizer with knowledge acquired from robust nlp techniques. Mani, and MT Maybury. Advances in automatic text summarization, 4-5.
  12. Bookstein, A., Klein, S. T., & Raita, T. (1995, July). Detecting content-bearing words by serial clustering. In Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 319-327). ACM.
  13. Ganapathiraju, M. K. (2002). Overview of summarization methods. Selfpaced lab in Information Retrieval.
  14. Miller, G. A. (1995). WordNet: a lexical database for English.Communications of the ACM, 38(11), 39-41.
  15. Lin, C. Y., & Hovy, E. (1997, March). Identifying topics by position. InProceedings of the fifth conference on Applied natural language processing(pp. 283-290). Association for Computational Linguistics.
  16. García-Hernández, R. A., & Ledeneva, Y. (2009, February). Word sequence models for single text summarization. In Advances in Computer-Human Interactions, 2009. ACHI'09. Second International Conferences on (pp. 44-48). IEEE.
  17. Zhang, Y., Zincir-Heywood, N., & Milios, E. (2005, November). Narrative text classification for automatic key phrase extraction in web document corpora. In Proceedings of the 7th annual ACM international workshop on Web information and data management (pp. 51-58). ACM.
  18. Kruengkrai, C., & Jaruskulchai, C. (2003, October). Generic text summarization using local and global properties of sentences. In Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on (pp. 201-206). IEEE.
  19. Wang, M., Wang, X., & Xu, C. (2005, October). An approach to concept-obtained text summarization. In IEEE International Symposium on Communications and Information Technology, 2005. ISCIT 2005. (Vol. 2, pp. 1337-1340). IEEE.
  20. Zamanifar, A., Minaei-Bidgoli, B., & Sharifi, M. (2008, August). A new hybrid farsi text summarization technique based on term co-occurrence and conceptual property of the text. In Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD'08. Ninth ACIS International Conference on (pp. 635-639). IEEE.
  21. Lin, C. Y. (1999, November). Training a selection function for extraction. InProceedings of the eighth international conference on Information and knowledge management (pp. 55-62). ACM.
  22. Suanmali, L., Binwahlan, M. S., & Salim, N. (2009, August). Sentence features fusion for text summarization using fuzzy logic. In Hybrid Intelligent Systems, 2009. HIS'09. Ninth International Conference on (Vol. 1, pp. 142-146). IEEE.
  23. Conroy, J. M., & O'leary, D. P. (2001, September). Text summarization via hidden markov models. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 406-407). ACM.
  24. Evans, D. K. (2005). Identifying similarity in text: multi-lingual analysis for summarization (Doctoral dissertation, Columbia University).
  25. Cowie, J., Mahesh, K., Nirenburg, S., & Zajaz, R. (1998). MINDS-Multilingual INteractive document summarization. In Working Notes of the AAAI Spring Symposium on Intelligent Text Summarization (pp. 131-132).
  26. Radev, D. R., Allison, T., Blair-Goldensohn, S., Blitzer, J., Celebi, A., Dimitrov, S., ... & Otterbacher, J. (2004, May). MEAD-A Platform for Multidocument Multilingual Text Summarization. In LREC.
  27. Kaikhah, K. (2004). Text summarization using neural networks.
  28. Suanmali, L., Salim, N., & Binwahlan, M. S. (2009). Fuzzy logic based method for improving text summarization. arXiv preprint arXiv:0906.4690.
  29. Osborne, M. (2002, July). Using maximum entropy for sentence extraction. In Proceedings of the ACL-02 Workshop on Automatic Summarization-Volume 4 (pp. 1-8). Association for Computational Linguistics.
  30. Mackeown, K., Nenkova, A., Elson, D., Passonneau, R., & Hirschberg, J. (2005). A task based evaluation of multidocument system. In SIGIR (Vol. 5, pp. 210-217).
  31. Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), 60-76.
  32. Erkan, G., & Radev, D. R. (2004). LexRank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research,22, 457-479.
  33. Hahn, U., & Romacker, M. (2001, March). The SYNDIKATE text Knowledge base generator. In Proceedings of the first international conference on Human language technology research (pp. 1-6). Association for Computational Linguistics.
  34. Nenkova, A., & Passonneau, R. J. (2004, May). Evaluating Content Selection in Summarization: The Pyramid Method. In HLT-NAACL (Vol. 4, pp. 145-152).
  35. Berry Michael, W. (2004). Automatic Discovery of Similar Words. Survey of Text Mining: Clustering, Classification and Retrieval”, Springer Verlag, New York, 200, 24-43.
  36. Lin, C. Y. (2004, July). Rouge: A package for automatic evaluation of summaries. In Text summarization branches out: Proceedings of the ACL-04 workshop (Vol. 8).
  37. Hovy, E., Lin, C. Y., Zhou, L., & Fukumoto, J. (2006, May). Automated summarization evaluation with basic elements. In Proceedings of the Fifth Conference on Language Resources and Evaluation (LREC 2006) (pp. 604-611). Genoa, Italy.
  38. Chan, S. W., Lai, T. B., Gao, W. J., & T'sou, B. K. (2000, April). Mining discourse markers for Chinese textual summarization. In Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization (pp. 11-20). Association for Computational Linguistics.
  39. Patil, A., Pharande, K., Nale, D., & Agrawal, R. (2015). Automatic text summarization. International Journal of Computer Applications, 109(17).
  40. Hachey, B. (2009, August). Multi-document summarisation using generic relation extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1 (pp. 420-429). Association for Computational Linguistics.
  41. Babar, S. A., & Patil, P. D. (2015). Improving Performance of Text Summarization. Procedia Computer Science, 46, 354-363.
  42. Pembe, F. C., & Güngör, T. (2007, June). Automated Querybiased and Structure-preserving Text Summarization on Web Documents. InProceedings of the International Symposium on Innovations in Intelligent Systems and Applications, İstanbul.
  43. Varadarajan, R., & Hristidis, V. (2005, October). Structure-based query-specific document summarization. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 231-232). ACM.
  44. Barzilay, R., & Elhadad, M. Using Lexical Chains for Text Summarization.
  45. Steinberger, J., & Ježek, K. (2012). Evaluation measures for text summarization. Computing and Informatics, 28(2), 251-275.
  46. Lin, J., Ozsu, M., & Liu, L. (2009). Summarization. Encyclopedia of Database Systems.
  47. Cheung, J. C. (2008). Comparing Abstractive and Extractive Summarization of Evaluative Text: Controversiality and Content Selection (Doctoral dissertation, UNIVERSITY OF BRITISH COLUMBIA).
  48. Ramanujam, N., & Kaliappan, M. (2016). An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy. The Scientific World Journal, 2016.
  49. Abbasi-ghalehtaki, R., Khotanlou, H., & Esmaeilpour, M. (2016). Fuzzy evolutionary cellular learning automata model for text summarization. Swarm and Evolutionary Computation.
  50. Alguliev, R., & Aliguliyev, R. (2009). Evolutionary algorithm for extractive text summarization. Intelligent Information Management, 1(02), 128.
  51. Marujo, L., Ling, W., Ribeiro, R., Gershman, A., Carbonell, J., de Matos, D. M., & Neto, J. P. (2016). Exploring events and distributed representations of text in multi-document summarization. Knowledge-Based Systems, 94, 33-42.
  52. Baralis, E., Cagliero, L., Mahoto, N., & Fiori, A. (2013). GRAPHSUM: Discovering correlations among multiple terms for graph-based summarization. Information Sciences, 249, 96-109.
  53. Yang, L., Cai, X., Zhang, Y., & Shi, P. (2014). Enhancing sentence-level clustering with ranking-based clustering framework for theme-based summarization. Information sciences, 260, 37-50.
Index Terms

Computer Science
Information Sciences

Keywords

Text Summarization Extractive summarization

Powered by PhDFocusTM