Automatic text summarization using support vector machine

International Journal of Innovative Computing, Information and Control

Volumn 5, Issue 7, 2009, Pages 1987-1996

  Begum, Nadira ^a   Fattah, Mohamed Abdel ^a,b   Ren, Fuji ^a,c  

^a UNIVERSITY OF TOKUSHIMA   (Japan)

^b HELWAN UNIVERSITY   (Egypt)

^c BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Automatic summarization; Support vector machine; Text features

Indexed keywords

AUTOMATIC SUMMARIZATION; AUTOMATIC TEXT SUMMARIZATION; COMPRESSION RATES; NUMERICAL DATA; RELATIVE LENGTH; SCORE FUNCTION; TEXT FEATURE; TEXT FEATURES;

IMAGE RETRIEVAL; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 67749132539     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (22)

1. S. Hobson, B. Dorr, C. Monz and R. Schwartz, Task-based evaluation of text summarization using Relevance Prediction, Information Processing & Management, vol.43, no.6, pp.1482-1499, 2007.
2. J. Sjöbergh, Older versions of the ROUGEeval summarization evaluation system were easier to fool, Information Processing & Management, vol.43, no.6, pp.1500-1505, 2007.
3. P. Over, H. Dang and D. Harman, DUC in context, Information Processing & Management, vol.43, no.6, pp.1506-1520, 2007.
4. T. Hirao, M. Okumura, N. Yasuda and H. Isozaki, Supervised automatic evaluation for summarization with voted regression model, Information Processing & Management, vol.43, no.6, pp.1521-1535, 2007.
5. D. Zajic, B. Dorr, J. Lin and R. Schwartz, Multi-candidate reduction: sentence compression as a tool for document summarization tasks, Information Processing & Management, vol.43, no.6, pp.1549-1570, 2007.
6. T. Nomoto, Discriminative sentence compression with conditional random fields, Information Processing & Management, vol.43, no.6, pp.1571-1587, 2007.
7. L. Vanderwende, H. Suzuki, C. Brockett and A. Nenkova, Beyond Sum Basic: task-focused summarization with sentence simplification and lexical expansion, Information Processing & Management, vol.43, no.6, pp.1606-1618, 2007.
8. S. Harabagiu, A. Hickl and F. Lacatusu, Satisfying information needs with multi-document summaries, Information Processing & Management, vol.43, no.6, pp.1619-1642, 2007.
9. M. Moens, Summarizing court decisions, Information Processing & Management, vol.43, no.6, pp.1748-1764, 2007.
10. L. Reeve, H. Han and A. Brooks, The use of domain-specific concepts in biomedical text summarization, Information Processing & Management, vol.43, no.6, pp.1765-1776, 2007.
11. X. Ling, J. Jiang, X. He, Q. Mei, C. Zhai and B. Schatz, Generating gene summaries from biomedical literature: a study of semi-structured summarization, Information Processing & Management, vol.43, no.6, pp.1777-1791, 2007.
12. G. Salton, A. Singhal, M. Mitra and C. Buckley, Automatic text structuring and summarization, Information Processing & Management, vol.33, no.2, pp.193-207, 1997.
13. S. Mukherjee, E. Osuna and F. Girosi, Nonlinear prediction of chaotic time series using support vector machine, Proc. of the IEEE Workshop on Neural Networks for Signal Processing 7, Amerlia Island, FL, pp.511-519, 1997.
14. E. Osuna, R. Freund and F. Girosi, An improved training algorithm for support vector machines, Proc. of the IEEE Workshop on Neural Networks for Signal Processing VII, New York, pp.276-285, 1997.
15. M. Brown, H. G. Lewis and S. R. Gunn, Linear spectral mixture models and support vector machines for remote sensing, IEEE Transactions on Geoscience and Remote Sensing, vol.38, no.5, 2000.
16. G. M. Foody and A. Mathur, A relative evaluation of multiclass image classification by support vector machines, IEEE Transactions on Geoscience and Remote Sensing, vol.42, no.6, 2004.
17. Q. She, H. Su, L. Dong and J. Chu, Support vector machine with adaptive parameters in image coding, International Journal of Innovative Computing, Information and Control, vol.4, no.2, pp.359-368, 2008.
18. R. Chen and S. Chen, Intrusion detection using a hybrid support vector machine based on entropy and TF-IDF, International Journal of Innovative Computing, Information and Control, vol.4, no.2, pp.413-424, 2008.
19. X. Song, W. Chen and B. Jiang, Sample reducing method in support vector machine based on kclosest sub-clusters, International Journal of Innovative Computing, Information and Control, vol.4, no.7, pp.1751-1760, 2008.
20. M. G. Sohrab, M. A. Fattah and F. Ren, The best feature parameter and HMM for text summarization, Research in Computing Science, vol.34, pp.153-161, 2008.
21. F. Ren, Automatic abstracting important sentences, International Journal of Information Technology and Decision Making, vol.4, no.1, pp.141-152, 2005.
22. F. Ren and B. B. David, Advanced information retrieval, Electronic Notes in Theoretical Computer Science, vol.225, no.1, pp.303-317, 2009.