Purpose – Summarization of an entire web site with diverse content may lead to a summary heavily biased towards the site's dominant topics. The purpose of this paper is to present 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.
International Journal of Web Information Systems – Emerald Publishing
Published: Nov 23, 2010
Keywords: Programming and algorithm theory; Internet; Cluster analysis; Data handling