Beyond keyword and cue-phrase matching: A sentence-based abstraction technique for information extraction
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AbstractWith the explosion in the quantity of on-line text and multimedia information in recent years, there has been a renewed interest in the automated extraction of knowledge and information in various disciplines. In this paper, we provide a novel quantitative model for the creation of a summary by extracting a set of sentences that represent the most salient content of a text. The model is based on a shallow linguistic extraction technique. What distinguishes it from previous research is that it does not work on the detection of specific keywords or cue-phrases to evaluate the relevance of the sentence concerned. Instead, the attention is focused on the identification of the main factors in the textual continuity. Simulation experiments suggest that this technique is useful because it moves away from a purely keyword-based method of textual information extraction and its associated limitations. (c) 2005 Elsevier B.V. All rights reserved.
All Author(s) ListChan SWK
Journal nameDecision Support Systems
Volume Number42
Issue Number2
Pages759 - 777
LanguagesEnglish-United Kingdom
Keywordsautomatic summary; connectionist model; information extraction; shallow text processing
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Operations Research & Management Science; OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Last updated on 2020-25-03 at 00:48