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Issue No.03 - March (2008 vol.20)
pp: 411-424
ABSTRACT
Given a user keyword query, current Web search engines return a list of individual web pages ranked by their "goodness" with respect to the query. Thus the basic unit for search and retrieval is an individual page, even though information on a topic is often spread across multiple pages. This degrades the quality of search results especially for long or uncorrelated (multi-topic) queries (in which individual keywords rarely occur together in the same document) where a single page is unlikely to satisfy the user's information need. We propose a technique that given a keyword query, on-the-fly generates new pages, called composed pages, which contain all query keywords. The composed pages are generated by extracting and stitching together relevant pieces from hyperlinked Web pages, and retaining links to the original Web pages. To rank the composed pages we consider both the hyperlink structure of the original pages, as well as the associations between the keywords within each page. Furthermore, we present and experimentally evaluate heuristic algorithms to efficiently generate the top composed pages. The quality of our method is compared to current approaches using user surveys. Finally, we also show how our techniques can be used to perform query-specific summarization of web pages.
INDEX TERMS
Internet search, Retrieval Models, Search process, Web Search
CITATION
Ramakrishna Varadarajan, Vagelis Hristidis, Tao Li, "Beyond Single-Page Web Search Results", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 3, pp. 411-424, March 2008, doi:10.1109/TKDE.2007.190703
REFERENCES
[1] J. Abracos and G. Pereira-Lopes, “Statistical Methods for Retrieving Most Significant Paragraphs in Newspaper Articles,” Proc. ACL/EACL Workshop Intelligent Scalable Text Summarization, 1997.
[2] S. Agrawal, S. Chaudhuri, and G. Das, “DBXplorer: A System for Keyword-Based Search over Relational Databases,” Proc. 18th IEEE Int'l Conf. Data Eng. (ICDE), 2002.
[3] E. Amitay and C. Paris, “Automatically Summarizing Web Sites—Is There Any Way around It,” Proc. Ninth ACM Int'l Conf. Information and Knowledge Management (CIKM), 2000.
[4] A. Balmin, V. Hristidis, and Y. Papakonstantinou, “Authority-Based Keyword Queries in Databases Using ObjectRank,” Proc. 30th Int'l Conf. Very Large Data Bases (VLDB), 2004.
[5] R. Barzilay and M. Elhadad, “Using Lexical Chains for Text Summarization,” Proc. Intelligent Scalable Text Summarization Workshop (ISTS), 1997.
[6] A.L. Berger and V.O. Mittal, “OCELOT: A System for Summarizing Web Pages,” Proc. ACM SIGIR, 2000.
[7] G. Bhalotia, C. Nakhe, A. Hulgeri, S. Chakrabarti, and S. Sudarshan, “Keyword Searching and Browsing in Databases Using BANKS,” Proc. 18th IEEE Int'l Conf. Data Eng. (ICDE), 2002.
[8] D. Cai, X. He, J. Wen, and W. Ma, “Block-Level Link Analysis,” Proc. ACM SIGIR, 2004.
[9] H.H. Chen, J.J. Kuo, and T.C. Su, “Clustering and Visualization in a Multilingual Multidocument Summarization System,” Proc. 25th European Conf. Information Retrieval Research (ECIR), 2003.
[10] Proc. Document Understanding Conf., http:/duc.nist.gov, 2005.
[11] H.P. Edmundson, “New Methods in Automatic Abstracting,” J.ACM, vol. 16, no. 2, pp. 264-285, 1969.
[12] G. Erkan and D.R. Radev, “Lexrank: Graph-Based Centrality as Salience in Text Summarization,” J. Artificial Intelligence Research, vol. 22, pp. 457-479, 2004.
[13] T. Fukusima and M. Okumura, Text Summarization Challenge Text Summarization Evaluation in Japan, 2001.
[14] R. Goldman, N. Shivakumar, S. Venkatasubramanian, and H. Garcia-Molina, “Proximity Search in Databases,” Proc. 24th Int'l Conf. Very Large Data Bases (VLDB), 1998.
[15] J. Goldstein, M. Kantrowitz, V. Mittal, and J. Carbonell, “Summarizing Text Documents: Sentence Selection and Evaluation Metrics,” Proc. ACM SIGIR, 1999.
[16] Google Desktop Search, http:/desktop.google.com/, 2007.
[17] L. Guo, F. Shao, C. Botev, and J. Shanmugasundaram, “XRANK: Ranked Keyword Search over XML Documents,” Proc. ACM SIGMOD, 2003.
[18] M.A. Hearst, “Using Categories to Provide Context for Full-Text Retrieval Results,” Proc. Intelligent Multimedia Information Retrieval Systems and Management (RIAO), 1994.
[19] E. Hovy and C.Y. Lin, “The Automated Acquisition of Topic Signatures for Text Summarization,” Proc. 18th Int'l Conf. Computational Linguistics (COLING), 2000.
[20] V. Hristidis, L. Gravano, and Y. Papakonstantinou, “Efficient IR-Style Keyword Search over Relational Databases,” Proc. 29th Int'l Conf. Very Large Data Bases (VLDB), 2003.
[21] V. Hristidis and Y. Papakonstantinou, “DISCOVER: Keyword Search in Relational Databases,” Proc. 28th Int'l Conf. Very Large Data Bases (VLDB), 2002.
[22] V. Hristidis, Y. Papakonstantinou, and A. Balmin, “Keyword Proximity Search on XML Graphs,” Proc. 19th IEEE Int'l Conf. Data Eng. (ICDE), 2003.
[23] V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar, “Bidirectional Expansion for Keyword Search on Graph Databases,” Proc. 31st Int'l Conf. Very Large Data Bases (VLDB), 2005.
[24] B. Kimelfeld and Y. Sagiv, “Finding and Approximating Top-$k$ Answers in Keyword Proximity Search,” Proc. 25th ACM Symp. Principles of Database Systems (PODS), 2006.
[25] J. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” J. ACM, vol. 46, no. 5, pp. 604-632, 1999.
[26] W.S. Li, K.S. Candan, Q. Vu, and D. Agrawal, “Retrieving and Organizing Web Pages by “Information Unit”,” Proc. 10th Int'l World Wide Web Conf. (WWW), 2001.
[27] D. Marcu, “Discourse Trees Are Good Indicators of Importance in Text,” Advances in Automatic Text Summarization, 1999.
[28] R. Mihalcea and P. Tarau, “TextRank: Bringing Order into Texts,” Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP), 2004.
[29] MSN Desktop Search, http:/toolbar.msn.com/, 2007.
[30] Oracle interMedia, http://www.oracle.com/technology/pro ducts intermedia, 2007.
[31] L. Page, S. Brin, R. Motwani, and T. Winograd, “The Pagerank Citation Ranking: Bringing Order to the Web,” technical report, Stanford Univ., 1998.
[32] D.R. Radev and K.R. McKeown, “Generating Natural Language Summaries from Multiple Online Sources,” Computational Linguistics, vol. 24, no. 3, pp. 470-500, 1998.
[33] D.R. Radev, W. Fan, and Z. Zhang, “WebInEssence: A Personalized Web-Based Multidocument Summarization and Recommendation System,” Proc. NAACL Workshop Automatic Summarization, 2001.
[34] G. Salton, A. Singhal, M. Mitra, and C. Buckley, “Automatic Text Structuring and Summarization,” Information Processing and Management, vol. 33, no. 2, pp. 193-207, 1997.
[35] A. Singhal, “Modern Information Retrieval: A Brief Overview,” IEEE Data Eng. Bull., 2001.
[36] R. Song, H. Liu, J. Wen, and W. Ma, “Learning Block Importance Models for Web Pages,” Proc. 13th Int'l World Wide Web Conf. (WWW), 2004.
[37] A. Tombros and M. Sanderson, “Advantages of Query-Biased Summaries in Information Retrieval,” Proc. ACM SIGIR, 1998.
[38] R. Varadarajan and V. Hristidis, “A System for Query-Specific Document Summarization,” Proc. 15th ACM Int'l Conf. Information and Knowledge Management (CIKM), 2006.
[39] R. Varadarajan, V. Hristidis, and T. Li, “Searching the Web Using Composed Pages,” Proc. ACM SIGIR, 2006.
[40] R.W. White, I. Ruthven, and J.M. Jose, “Finding Relevant Documents Using Top Ranking Sentences: An Evaluation of Two Alternative Schemes,” Proc. ACM SIGIR, 2002.
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