LEADER 05257nam 2200637 450 001 9910140506903321 005 20211108203506.0 010 $a1-119-04407-3 010 $a1-119-00475-6 010 $a1-119-04414-6 035 $a(CKB)2670000000569491 035 $a(EBL)1800890 035 $a(SSID)ssj0001374489 035 $a(PQKBManifestationID)11793686 035 $a(PQKBTitleCode)TC0001374489 035 $a(PQKBWorkID)11325215 035 $a(PQKB)10511752 035 $a(MiAaPQ)EBC1800890 035 $a(Au-PeEL)EBL1800890 035 $a(CaPaEBR)ebr10944998 035 $a(CaONFJC)MIL647919 035 $a(OCoLC)892044728 035 $a(PPN)19145575X 035 $a(EXLCZ)992670000000569491 100 $a20141016h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAutomatic text summarization /$fJuan-Manuel, Torres-Moreno 210 1$aLondon, [England] ;$aHoboken, New Jersey :$cISTE Limited :$cJohn Wiley & Sons,$d2014. 210 4$dİ2014 215 $a1 online resource (376 p.) 300 $aDescription based upon print version of record. 311 $a1-322-16662-5 311 $a1-84821-668-8 320 $aIncludes bibliographical references and index. 327 $aCover Page; Half-title Page; Title page; Copyright page; Contents; Foreword by A. Zamora and R. Salvador; Foreword; The need to identify important information; The problem of information storage; Automatic size reduction; The future; Foreword by H. Saggion; Automatic Text Summarization; Juan-Manual Torres-Moreno; Notation; Introduction; The need to summarize texts; The summarization process; Automatic text summarization; About this book; PART 1: Foundations; 1: Why Summarize Texts?; 1.1. The need for automatic summarization; 1.2. Definitions of text summarization 327 $a1.3. Categorizing automatic summaries1.4. Applications of automatic text summarization; 1.5. About automatic text summarization; 1.6. Conclusion; 2: Automatic Text Summarization: Some Important Concepts; 2.1. Processes before the process; 2.1.1. Sentence-term matrix: the vector space model (VSM) model; 2.2. Extraction, abstraction or compression?; 2.3. Extraction-based summarization; 2.3.1. Surface-level algorithms; 2.3.2. Intermediate-level algorithms; 2.3.3. Deep parsing algorithms; 2.4. Abstract summarization; 2.4.1. FRUMP or the temptation to understand 327 $a2.4.2. Information extraction and abstract generation2.5. Sentence compression and fusion; 2.5.1. Sentence compression; 2.5.2. Multisentence fusion; 2.6. The limits of extraction; 2.6.1. Cohesion and coherence; 2.6.2. The HexTAC experiment; 2.7. The evolution of automatic text summarization tasks; 2.7.1. Traditional tasks; Single-document summarization; Guided summarization; Multidocument summarization; Multilingual summarization; 2.7.2. Current and future problems; Summarization based on the source of the document; Specialized-domain summarization; Update summarization 327 $aSentence compression and multi-sentence fusionSemantic summarization; Opinion summarization; Multi and cross-lingual summarization; Ultra-summarization; Tweet summarization (short texts in microblogs); Multimedia summarization; Abstract generation; 2.8. Evaluating summaries; 2.9. Conclusion; 3: Single-document Summarization; 3.1. Historical approaches; 3.1.1. H.P. Luhn's Automatic Creation of Literature Abstracts; 3.1.2. The Luhn algorithm; 3.1.2.1. Preprocessing; 3.1.2.2. Sentence weighting; 3.1.3. Edmundson's linear combination; Edmundson's algorithm; 3.1.4. Extracts by elimination 327 $a3.2. Machine learning approaches3.2.1. Machine learning parameters; 3.3. State-of-the-art approaches; 3.4. Latent semantic analysis; 3.4.1. Singular value decomposition (SVD); 3.4.2. Sentence weighting by SVD; 3.5. Graph-based approaches; 3.5.1. PAGERANK and SNA algorithms; 3.5.2. Graphs and automatic text summarization; 3.5.3. Constructing the graph; 3.5.4. Sentence weighting; 3.5.4.1. LEXRANK; 3.5.4.2. TEXTRANK; 3.6. DIVTEX: a summarizer based on the divergence of probability distribution; 3.7. CORTEX 22; 3.7.1. Frequential measures; 3.7.2. Hamming measures; 3.7.3. Mixed measures 327 $a3.7.4. Decision algorithm 330 $aThis new textbook examines the motivations and the different algorithms for automatic document summarization (ADS). We performed a recent state of the art. The book shows the main problems of ADS, difficulties and the solutions provided by the community. It presents recent advances in ADS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several exemples are included in order to clarify the theoretical concepts. The books currently available in the area of Automatic Document Summarization are not recent. Powerful algorithms have been develop 606 $aAutomatic abstracting 615 0$aAutomatic abstracting. 676 $a025.04 700 $aTorres-Moreno$b Juan-Manuel$01068810 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140506903321 996 $aAutomatic text summarization$92553786 997 $aUNINA