a member of the AAAI/SIGART Doctoral Consortium Committee (1997Committee ( -2001 and he is co-editor-in-chief of the journal Computational Intelligence. He has published on the processing, interpretation and use of visual and range signals for landmark-based navigation and map construction in single-and multiagent robotics. His current research activity is centered on modelling and mining of content and link structure of Networked Information Spaces. A. Nur Zincir-Heywood received the Ph.D degree in network information retrieval from the Department of Computer Engineering, Ege University, Izmir, Turkey, in 1998. She is an Associate Professor with the Computer Science Department, Dalhousie University, Halifax, Canada. From 1996 to 1997, she was a Visiting Researcher at the IIMS Research Center, School of Engineering, University of Sussex, Brighton, U.K. Previous to her current position, she was an Assistant Professor with the Department of Computer Engineering, Ege University (1998 -2000). She has also been involved with Network Technology Workshops of Internet Society as an Instructor from 1997 to 2000. Her research interests include network traffic mining and analysis, network operation and management, network/systems security and network information systems. She has published over hundred conference and journal papers in these areas, and has been involved in projects concerning network operations and information systems. Dr. Zincir-Heywood is a member of the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronic Engineers (IEEE). TOPIC-BASED WEB SITE SUMMARIZATION Abstract: Purpose -Summarization of an entire Web site with diverse content may lead to a summary heavily biased towards the site's dominant topics. This paper presents a novel topic-based framework to address this problem. Design/methodology/approach -A two-stage framework is proposed. The first stage identifies the main topics covered in a Web site via clustering and the second stage summarizes each topic separately. The proposed system is evaluated by a user study, and compared with the single-topic summarization approach. Findings -The user study demonstrates that the clustering-summarization approach statistically significantly outperforms the plain summarization approach in the multi-topic Web site summarization task. Text-based clustering based on selecting features with high variance over Web pages is reliable, Outgoing links are useful if a rich set of cross links is available. Research limitations/implications -More sophisticated clustering methods than those used in this study are worth investigating. The proposed method should be tested on web content that is less structured than organizational web sites, for example blogs. Practical implications -The proposed summarization framework can be applied to the effective organization of search engine results and faceted or topical browsing of large Web sites. Originality/value -Several key components are integrated for web site summarization for the first time, including feature selection and link analysis, key phrase and key sentence extraction. Insight into the contributions of links and content to topic-based summarization was gained. A classification approach is used to minimize the number of parameters.
This is also similar to the approach suggested in REF which focuses on identifying main topics in a text and then summarizing each topic independently.
TOPIC-BASED WEB SITE SUMMARIZATION
IJWIS
IJWIS
Computer Science