05083nam 22008655 450 99646570750331620200701045055.03-540-45224-910.1007/978-3-540-45224-9(CKB)1000000000212129(SSID)ssj0000324268(PQKBManifestationID)11236838(PQKBTitleCode)TC0000324268(PQKBWorkID)10304907(PQKB)10953563(DE-He213)978-3-540-45224-9(MiAaPQ)EBC6302754(MiAaPQ)EBC5579426(Au-PeEL)EBL5579426(OCoLC)1066185716(PPN)155227041(EXLCZ)99100000000021212920131124d2003 u| 0engurnn#008mamaatxtccrKnowledge-Based Intelligent Information and Engineering Systems[electronic resource] 7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003, Proceedings, Part I /edited by Vasile Palade1st ed. 2003.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2003.1 online resource (CII, 1476 p.)Lecture Notes in Artificial Intelligence ;2773Bibliographic Level Mode of Issuance: Monograph3-540-40803-7 Includes bibliographical references and index.Keynote Lectures -- General Session Papers -- Invited Sessions Papers.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].Lecture Notes in Artificial Intelligence ;2773Artificial intelligenceComputer communication systemsInformation storage and retrievalApplication softwareUser interfaces (Computer systems)Information technologyBusiness—Data processingArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computer Communication Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/I13022Information Storage and Retrievalhttps://scigraph.springernature.com/ontologies/product-market-codes/I18032Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040User Interfaces and Human Computer Interactionhttps://scigraph.springernature.com/ontologies/product-market-codes/I18067IT in Businesshttps://scigraph.springernature.com/ontologies/product-market-codes/522000Artificial intelligence.Computer communication systems.Information storage and retrieval.Application software.User interfaces (Computer systems).Information technology.Business—Data processing.Artificial Intelligence.Computer Communication Networks.Information Storage and Retrieval.Information Systems Applications (incl. Internet).User Interfaces and Human Computer Interaction.IT in Business.006.3Palade Vasileedthttp://id.loc.gov/vocabulary/relators/edtInternational Conference on Knowledge-Based Intelligent Information and Engineering SystemsMiAaPQMiAaPQMiAaPQBOOK996465707503316Knowledge-Based Intelligent Information and Engineering Systems771988UNISA