Number of found records: 40
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LIANG, Shao-Fen; DEVLIN, Siobhan; TAIT, John |
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Can Automatic Abstracting Improve on Current Extracting Techniques in Aiding Users to Judge the Relevance of Pages in Search Engine Results? |
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Current search engines use sentence extraction techniques to produce snippet result summaries, which users may find less than ideal for determining the relevance of pages. Unlike extracting, abstracting programs analyse the context of documents and rewrite them into informative summaries. Our project aims to produce abstracting summaries which are coherent and easy to read thereby lessening users' time in judging the relevance of pages. However, automatic abstracting technique has its domain restriction. For solving this problem we propose to employ text classification techniques. We propose a new approach to initially classify whole web documents into sixteen top level ODP categories by using machine learning and a Bayesian classifier. We then manually create sixteen templates for each category. The summarisation techniques we use include a natural language processing techniques to weight words and analyse lexical chains to identify salient phrases and place them into relevant template slots to produce summaries (AU) |
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extraction techniques; context analysis; informative summary; text classification techniques; natural language processing |
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MOENS, Marie-Francine; DUMORTIER, Jos |
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Use of a text grammar for generating highlight abstracts of magazine articles. |
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Journal of Documentation, 2000, vol.56, n.5, pp.520-39. |
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On line (11/05/2005) |
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Browsing a database of article abstracts is one way to select and buy relevant magazine articles online. Our research contributes to the design and development of text grammars for abstracting texts in unlimited subject domains. We developed a system that parses texts based on the text grammar of a specific text type and that extracts sentences and statements which are relevant for inclusion in the abstracts. The system employs knowledge of the discourse patterns that are typical of news stories. The results are encouraging and demonstrate the importance of discourse structures in text summarisation (DB) |
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Text analysis; Automatic abstracting (LISA) |
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NOMOTO, Tadashi; MATSUMOTO, Yuji |
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Data Reliability and Its Effects on Automatic Abstracting. |
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Fifth workshop on Verylarge Corpora, Japan, 1997 |
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We discuss a particular approach to automatic abstracting, where an abstract is created by extracting important sentences from a text. A primary purpose of the paper is to demonstrate that the reliability of human supplied annotations on corpora has crucial effects on how well an automatic abstracting system performs. The corpus is developed through human judgements on possible summary sentences in a text. The reliability of human judgements is evaluated by the kappa statistic, a reliability metric standardly used in behavioral sciences. The C4.5 decision tree method (Quinlan, 1993) is used to build a extraction model. We demonstrate that there is a positive correlation of data reliability with a performance of automatic abstracting, and show results indicating that the reliability of human provided data is crucial for improving the performance of automatic abstracting. (AU) |
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automatic abstracting; extracting; tree method; extraction model; Kappa statistics |
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OAKES, Michael P.; PAICE, Chris. D. |
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The Automatic Generation of Templates for Automatic Abstracting |
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21st BCS IRSG Colloquium on IR, Glasgow, 1999 |
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Our goal is the automatic abstraction of journal articles, initially in the field of crop protection. We build a set of templates against which the original text is compared. The templates are designed so that they match the text at points of high information content, where inferences can be made about which expressions best reflect the content of the document. Strings found by matching templates are assigned roles specific to each template. These roles correspond to slots in a frame which is used to represent the document as a whole. An abstract is generated which contains the concept-strings selected from the text. (AU) |
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automatic abstracting; templates; concept-strings |
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