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Knowledge-Based Intelligent Information and Engineering Systems

7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003, Proceedings, Part I

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Vasile Palade

    1. Computing Laboratory, Oxford University, United Kingdom

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  2. Robert J. Howlett

    1. Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, Brighton, United Kingdom

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  3. Lakhmi Jain

    1. Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, Adelaide, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2773)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: KES 2003.

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Table of contents (196 papers)

  1. Front Matter

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  3. General Session Papers

    1. Knowledge-Based Systems

      1. A Unified Model Maintains Knowledge Base Integrity
        • John Debenham
        Pages 21-27
      2. Formal Argumentation Frameworks for the Extended Generalized Annotated Logic Programs
        • Takehisa Takahashi, Yuichi Umeda, Hajime Sawamura
        Pages 28-38
      3. Specifying and Validating Reactive Systems with CommonKADS Methodology
        • Maamar El-Amine Hamri, Claudia Frydman, Lucile Torres
        Pages 39-44
      4. TMMT: Tool Supporting Knowledge Modelling
        • Guy Camilleri, Jean-Luc Soubie, Joseph Zalaket
        Pages 45-52
      5. State-Based Planning with Numerical Knowledge
        • Joseph Zalaket, Guy Camilleri
        Pages 53-60
      6. KAMET II: An Extended Knowledge-Acquisition Methodology
        • Osvaldo Cairó, Julio César Alvarez
        Pages 61-67
      7. Automated Knowledge Acquisition by Relevant Reasoning Based on Strong Relevant Logic
        • Jingde Cheng
        Pages 68-80
      8. ConcepTool: Intelligent Support to the Management of Domain Knowledge
        • Ernesto Compatangelo, Helmut Meisel
        Pages 81-88
      9. Combining Revision Production Rules and Description Logics
        • Chan Le Duc, Nhan Le Thanh
        Pages 89-98
      10. Knowledge Support for Modeling and Simulation
        • Michal Ševčenko
        Pages 99-103
      11. Expert System for Simulating and Predicting Sleep and Alertness Patterns
        • Udo Trutschel, Rainer Guttkuhn, Anneke Heitmann, Acacia Aguirre, Martin Moore-Ede
        Pages 104-110
      12. Two Expert Diagnosis Systems for SMEs: From Database-Only Technologies to the Unavoidable Addition of AI Techniques
        • Sylvain Delisle, Josée St-Pierre
        Pages 111-125
      13. Using Conceptual Decision Model in a Case Study
        • Miki Sirola
        Pages 126-133
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  1. Knowledge-Based Intelligent Information and Engineering Systems

  2. Knowledge-Based Intelligent Information and Engineering Systems

Keywords

About this book

2.1 Text Summarization “Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks)” [3]. Basic and classical articles in text summarization appear in “Advances in automatic text summarization” [3]. A literature survey on information extraction and text summarization is given by Zechner [7]. In general, the process of automatic text summarization is divided into three stages: (1) analysis of the given text, (2) summarization of the text, (3) presentation of the summary in a suitable output form. Titles, abstracts and keywords are the most common summaries in Academic papers. Usually, the title, the abstract and the keywords are the first, second, and third parts of an Academic paper, respectively. The title usually describes the main issue discussed in the study and the abstract presents the reader a short description of the background, the study and its results. A keyword is either a single word (unigram), e.g.: ‘learning', or a collocation, which means a group of two or more words, representing an important concept, e.g.: ‘machine learning', ‘natural language processing'. Retrieving collocations from text was examined by Smadja [5] and automatic extraction of collocations was examined by Kita et al. [1].

Editors and Affiliations

  • Computing Laboratory, Oxford University, United Kingdom

    Vasile Palade

  • Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, Brighton, United Kingdom

    Robert J. Howlett

  • Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, Adelaide, Australia

    Lakhmi Jain

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