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  • × author_ss:"Goh, D.H."
  1. Ou, S.; Khoo, C.S.G.; Goh, D.H.: Multi-document summarization of news articles using an event-based framework (2006) 0.00 
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    Abstract
    Purpose - The purpose of this research is to develop a method for automatic construction of multi-document summaries of sets of news articles that might be retrieved by a web search engine in response to a user query. Design/methodology/approach - Based on the cross-document discourse analysis, an event-based framework is proposed for integrating and organizing information extracted from different news articles. It has a hierarchical structure in which the summarized information is presented at the top level and more detailed information given at the lower levels. A tree-view interface was implemented for displaying a multi-document summary based on the framework. A preliminary user evaluation was performed by comparing the framework-based summaries against the sentence-based summaries. Findings - In a small evaluation, all the human subjects preferred the framework-based summaries to the sentence-based summaries. It indicates that the event-based framework is an effective way to summarize a set of news articles reporting an event or a series of relevant events. Research limitations/implications - Limited to event-based news articles only, not applicable to news critiques and other kinds of news articles. A summarization system based on the event-based framework is being implemented. Practical implications - Multi-document summarization of news articles can adopt the proposed event-based framework. Originality/value - An event-based framework for summarizing sets of news articles was developed and evaluated using a tree-view interface for displaying such summaries.
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  2. Ou, S.; Khoo, S.G.; Goh, D.H.: Automatic multidocument summarization of research abstracts : design and user evaluation (2007) 0.00 
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    Abstract
    The purpose of this study was to develop a method for automatic construction of multidocument summaries of sets of research abstracts that may be retrieved by a digital library or search engine in response to a user query. Sociology dissertation abstracts were selected as the sample domain in this study. A variable-based framework was proposed for integrating and organizing research concepts and relationships as well as research methods and contextual relations extracted from different dissertation abstracts. Based on the framework, a new summarization method was developed, which parses the discourse structure of abstracts, extracts research concepts and relationships, integrates the information across different abstracts, and organizes and presents them in a Web-based interface. The focus of this article is on the user evaluation that was performed to assess the overall quality and usefulness of the summaries. Two types of variable-based summaries generated using the summarization method-with or without the use of a taxonomy-were compared against a sentence-based summary that lists only the research-objective sentences extracted from each abstract and another sentence-based summary generated using the MEAD system that extracts important sentences. The evaluation results indicate that the majority of sociological researchers (70%) and general users (64%) preferred the variable-based summaries generated with the use of the taxonomy.
    Type
    a
  3. Ou, S.; Khoo, C.; Goh, D.H.; Heng, H.-Y.: Automatic discourse parsing of sociology dissertation abstracts as sentence categorization (2004) 0.00 
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    Abstract
    We investigated an approach to automatic discourse parsing of sociology dissertation abstracts as a sentence categorization task. Decision tree induction was used for the automatic categorization. Three models were developed. Model 1 made use of word tokens found in the sentences. Model 2 made use of both word tokens and sentence position in the abstract. In addition to the attributes used in Model 2, Model 3 also considered information regarding the presence of indicator words in surrounding sentences. Model 3 obtained the highest accuracy rate of 74.5 % when applied to a test sample, compared to 71.6% for Model 2 and 60.8% for Model 1. The results indicated that information about sentence position can substantially increase the accuracy of categorization, and indicator words in earlier sentences (before the sentence being processed) also contribute to the categorization accuracy.
    Content
    1. Introduction This paper reports our initial effort to develop an automatic method for parsing the discourse structure of sociology dissertation abstracts. This study is part of a broader study to develop a method for multi-document summarization. Accurate discourse parsing will make it easier to perform automatic multi-document summarization of dissertation abstracts. In a previous study, we determined that the macro-level structure of dissertation abstracts typically has five sections (Khoo et al., 2002). In this study, we treated discourse parsing as a text categorization problem - assigning each sentence in a dissertation abstract to one of the five predefined sections or categories. Decision tree induction, a machine-learning method, was applied to word tokens found in the abstracts to construct a decision tree model for the categorization purpose. Decision tree induction was selected primarily because decision tree models are easy to interpret and can be converted to rules that can be incorporated in other computer programs. A well-known decision-tree induction program, C5.0 (Quinlan, 1993), was used in this study.
    Type
    a

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