Number of found records: 80
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WAN, Stephen |
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Summarization Resources |
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On line (11/05/2005) |
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Resources on automated summarization, conferences, methods |
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Automated summarization; resources |
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WHEATLEY, Alan; ARMSTRONG, C. J. |
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Survey of the Content and Characteristics of Electronic Abstracts. |
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PDF |
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The study had three investigative areas: an examination of current database producers' guidelines for their abstract writers, a brief survey of abstracts in some traditional online databases, and a detailed survey of abstracts from three types of electronic database (printsourced online databases, Internet subject trees or directories, and Internet gateways). The database producers, traditional online databases, and Internet databases were identified as representative of electronic information sources relevant to the higher education community in Britain, and were selected on the basis of both technical criteria and availability. Abstracts were investigated to secure quantitative determinations of their properties in two broad areas. Their content was examined to ascertain the abstracts' coverage of source document concepts, to quantify their depiction of source document elements such as bibliographies, figures and tables, and to see if they acknowledged any geographical constraints of source documents affecting their value for users. To assess their physical and readability properties, abstracts were subjected to readability testing software that measured primary characteristics such as total length, sentence length and word length, and applied several standard readability tests. (AU) |
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guidelines; abstracts; database |
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Yang, Christopher C.; Wang, Fu Lee |
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Hierarchical summarization of large documents |
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Journal of the American Society for Information Science and Technology, 2008, vol. 59, n. 6, pp. 887-902 |
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On line (Only UGR) |
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Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals - mathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents (AU) |
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Automatic abstracting; Automatic text analysis |
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Ye, Shiren; Chua, Tat-Seng; Kan, Min-Yen; Qiu, Long |
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Document concept lattice for text understanding and summarization |
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Information Processing & Management, Nov. 2007, Vol. 43 Issue 6, p1643-1662 |
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On line (04/2008) (Only UGR) |
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We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests. (DB) |
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Automatic abstracts; textural structure; semantics; |
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