Method and system for classifying display pages using summaries

Data processing: presentation processing of document – operator i – Presentation processing of document – Text summarization or condensation

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C715S234000, C715S277000

Reexamination Certificate

active

07392474

ABSTRACT:
A method and system for classifying display pages based on automatically generated summaries of display pages. A web page classification system uses a web page summarization system to generate summaries of web pages. The summary of a web page may include the sentences of the web page that are most closely related to the primary topic of the web page. The summarization system may combine the benefits of multiple summarization techniques to identify the sentences of a web page that represent the primary topic of the web page. Once the summary is generated, the classification system may apply conventional classification techniques to the summary to classify the web page. The classification system may use conventional classification techniques such as a Naïve Bayesian classifier or a support vector machine to identify the classifications of a web page based on the summary generated by the summarization system.

REFERENCES:
patent: 5317507 (1994-05-01), Gallant
patent: 5864855 (1999-01-01), Ruocco et al.
patent: 5918240 (1999-06-01), Kupiec et al.
patent: 6359633 (2002-03-01), Balasubramaniam et al.
patent: 6606644 (2003-08-01), Ford et al.
patent: 6609124 (2003-08-01), Chow et al.
patent: 7065707 (2006-06-01), Chen et al.
patent: 7120861 (2006-10-01), Marukawa
patent: 7137065 (2006-11-01), Huang et al.
patent: 2002/0062302 (2002-05-01), Oosta
patent: 2002/0087326 (2002-07-01), Lee et al.
patent: 2002/0138528 (2002-09-01), Gong et al.
patent: 2002/0138529 (2002-09-01), Yang-Stephens et al.
patent: 2002/0169770 (2002-11-01), Kim et al.
patent: 2003/0033274 (2003-02-01), Chow
patent: 2003/0221163 (2003-11-01), Glover et al.
patent: 2004/0153309 (2004-08-01), Lin et al.
Chen, J., Zhou, B., Shi, J., Zhang, H., and Fengwu, Q. 2001. Function-based object model towards website adaptation. In Proceedings of the 10th international Conference on World Wide Web (Hong Kong, Hong Kong, May 1-5, 2001). WWW '01. ACM Press, New York, NY, 587-596.
Joachims, T. 1998. Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. In Proceedings of the 10th European Conference on Machine Learning (Apr. 21-23, 1998). C. Nedellec and C. Rouveirol, Eds. Lecture Notes In Computer Science, vol. 1398. Springer-Verlag, London, 137-142.
Paice, C. D. 1981. The automatic generation of literature abstracts: an approach based on the identification of self-indicating phrases. In Proceedings of the 3rd Annual ACM Conference on Research and Development in information Retrieval (Cambridge, England, Jun. 23-27, 1980), pp. 159-165.
Copernic.com,“Copernic.com Summarization Technologies White Paper”, Dec. 2001, 7 pages.
Ananyan et al.,“Automated Analysis of Natural Language Texts”, Mar. 3, 2001, 6 pages.
Chen, F. R. and Bloomberg, D. S. 1997. Extraction of Indicative Summary Sentences from Imaged Documents. In Proceedings of the 4th international Conference on Document Analysis and Recognition (Aug. 18-20, 1997). ICDAR. IEEE Computer Society, Washington, DC, 227-232.
H. Saggion et al.,“Multi-Document Summarization by Cluster/Profile Relevance and Redundancy Removal,” presented at the HLT/NAACL Annual Meeting, pp. 1-8.
W. Jung et al.,“Automatic Text Summarization Using Two-Step Sentence Extraction,” in Lecture Notes in Computer Science, Feb. 14, 2005, pp. 71-81.
Y. Seki,“Sentence Extraction by tf/idf and POsition Weighting from Newspaper Articles,” 2003, Proc. 3rdNTCIR Workshop, 6 pages.
Buyukkokten, O., Garcia-Molina, H., and Paepcke, A. 2001. Seeing the whole in parts: text summarization for web browsing on handheld devices. In Proceedings of the 10th international Conference on World Wide Web (Hong Kong, May 1-5, 2001). WWW '01. ACM, New York, NY, pp. 652-662.
V. Hatzivassiloglou, J. L. Klavans, M. L. Holcombe, R. Barzilay, M.-Y. Kan, and K. R. McKeown. Simfinder: A flexible clustering tool for summarization. In NAACL Workshop on Automatic Summarization, pp. 41-49. Association for Computational Linguistics, 2001.
Gong, Yihong and Liu, Xin, “Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis,” SIGIR '01, Sep. 9-12, 2001, New Orleans, Louisiana, Copyright 2001 (7 pages).
Berger, Adam, L. and Mittal, Vibhu O., “OCELOT: A System for Summarizing Web Pages,” School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, Copyright 2000 (8 pages).
Schultz, J. Michael and Liberman, Mark “Topic Detection and Tracking Using idf-Weighted Cosine Coefficient,” University of Pennsylvania, Philadelphia, Pennsylvania, DARPA Broadcast News Workshop Proceedings, 1999 (5 pages).
Wang, Xuanhui et al., “Web Page Clustering Enhanced by Summarization,” CIKM, Online!, Nov. 8, 2004 (pp. 242-243).
Chue, W.L., et al., “SVD: A Novel Content-Based Representation Technique for Web Documents,” Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia, Proceedings of the Fourth INternaitonal Conference on Singapore, Dec. 15-18, 2003 (pp. 1840-1844).
Mazdak, Nima, “FarsiSum: A Persian Text Summarizer,” Master Thesis, OnLine!, Jan. 2004 (pp. 1-52).
European Search Report for EP Application No. 05 10 3580, Sep. 19, 2005 (3 pages).

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Method and system for classifying display pages using summaries does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method and system for classifying display pages using summaries, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and system for classifying display pages using summaries will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-2806704

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.