Beyond keyword and cue-phrase matching: A sentence-based abstraction technique for information extraction

With 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 extr...

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Vydáno v:Decision Support Systems Ročník 42; číslo 2; s. 759 - 777
Hlavní autor: Chan, Samuel W.K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.11.2006
Elsevier Science
Elsevier Sequoia S.A
Témata:
Applied sciences
Automatic summary
Computer science; control theory; systems
Connectionist model
Data processing. List processing. Character string processing
Exact sciences and technology
Information extraction
Information management
Memory organisation. Data processing
Shallow text processing
Simulation
Software
Studies
Connectionist model
Information extraction
Shallow text processing
Automatic summary
Textual data
Multimedia
Keyword
Continuous function
Text
Abstraction
Knowledge extraction
Modeling
Relevance
Linguistics
Knowledge discovery
Sentence
Quantitative analysis
Automatic
ISSN:0167-9236, 1873-5797
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Popis
Shrnutí:With 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.
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ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2004.11.017