Knowledge-Based Intelligent Information and Engineering Systems 7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003, Proceedings, Part I /

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 "Adv...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Palade, Vasile (Editor, http://id.loc.gov/vocabulary/relators/edt)
Format: Electronic eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
Edition:1st ed. 2003.
Series:Lecture Notes in Artificial Intelligence ; 2773
Subjects:
Online Access:Full Text via HEAL-Link
Description
Summary: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].
Physical Description:CII, 1476 p. online resource.
ISBN:9783540452249
DOI:10.1007/978-3-540-45224-9