000 | 06731cam a2200817Ma 4500 | ||
---|---|---|---|
001 | ocn892044728 | ||
003 | OCoLC | ||
005 | 20171224114739.0 | ||
006 | m o d | ||
007 | cr cnu---unuuu | ||
008 | 141003s2014 enk ob 001 0 eng d | ||
040 |
_aIDEBK _beng _epn _cIDEBK _dEBLCP _dN$T _dDG1 _dE7B _dYDXCP _dCDX _dOCLCQ _dRECBK _dOCLCF _dDEBSZ _dCOO _dOCLCQ _dSTF _dB24X7 _dOCLCO _dDEBBG _dD6H _dOCLCQ |
||
019 | _a959423368 | ||
020 |
_a9781119044147 _q(electronic bk.) |
||
020 |
_a1119044146 _q(electronic bk.) |
||
020 |
_a1322166625 _q(electronic bk.) |
||
020 |
_a9781322166629 _q(electronic bk.) |
||
020 |
_a9781119004752 _q(electronic bk.) |
||
020 |
_a1119004756 _q(electronic bk.) |
||
020 | _a1848216688 | ||
020 | _a9781848216686 | ||
020 | _z9781848216686 | ||
029 | 1 |
_aAU@ _b000053736693 |
|
029 | 1 |
_aCHBIS _b010441720 |
|
029 | 1 |
_aCHNEW _b000695430 |
|
029 | 1 |
_aCHNEW _b000695437 |
|
029 | 1 |
_aCHVBK _b334089069 |
|
029 | 1 |
_aDEBBG _bBV043397178 |
|
029 | 1 |
_aDEBSZ _b431789363 |
|
029 | 1 |
_aNZ1 _b15910039 |
|
035 |
_a(OCoLC)892044728 _z(OCoLC)959423368 |
||
050 | 4 | _aZ695.92 | |
072 | 7 |
_aLAN _x025000 _2bisacsh |
|
082 | 0 | 4 |
_a025.4/10285 _223 |
049 | _aMAIN | ||
100 | 1 | _aTorres-Moreno, Juan-Manuel. | |
245 | 1 | 0 |
_aAutomatic Text Summarization / _cJuan-Manuel Torres-Moreno. |
260 |
_aLondon : _bISTE ; _aHoboken, NJ : _bWiley, _c2014. |
||
300 | _a1 online resource. | ||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
490 | 1 | _aCognitive science and knowledge management series | |
588 | 0 | _aPrint version record. | |
504 | _aIncludes bibliographical references and index. | ||
520 |
_aTextual information in the form of digital documents quickly accumulates to create huge amounts of data. The majority of these documents are unstructured: it is unrestricted text and has not been organized into traditional databases. Processing documents is therefore a perfunctory task, mostly due to a lack of standards. It has thus become extremely difficult to implement automatic text analysis tasks. This book can help to process this ever-increasing, difficult-to-handle, mass of information. It examines the motivations and different algorithms for ATS. The author presents the recent state of the art before describing the main problems of ATS, as well as the difficulties and solutions provided by the community. It provides recent advances in ATS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several examples are also included in order to clarify the theoretical concepts. -- _cEdited summary from book. |
||
505 | 0 | _aTitle Page; Copyright; Foreword by A. Zamora and R. Salvador; Foreword by H. Saggion; Notation; Introduction; PART 1: Foundations; 1 Why Summarize Texts?; 1.1. The need for automatic summarization; 1.2. Definitions of text summarization; 1.3. Categorizing automatic summaries; 1.4. 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; 2.2. Extraction, abstraction or compression?; 2.3. Extraction-based summarization; 2.4. Abstract summarization. | |
505 | 8 | _a2.5. Sentence compression and fusion2.6. The limits of extraction; 2.7. The evolution of automatic text summarization tasks; 2.8. Evaluating summaries; 2.9. Conclusion; 3 Single-document Summarization; 3.1. Historical approaches; 3.2. Machine learning approaches; 3.3. State-of-the-art approaches; 3.4. Latent semantic analysis; 3.5. Graph-based approaches; 3.6. DIVTEX: a summarizer based on the divergence of probability distribution; 3.7. CORTEX22; 3.8. ARTEX: another summarizer based on the vectorial model; 3.9. ENERTEX: a summarization system based on textual energy. | |
505 | 8 | _a3.10. Approaches using rhetorical analysis3.11. Summarization by lexical chains; 3.12. Conclusion; 4 Guided Multi-Document Summarization; 4.1. Introduction; 4.2. The problems of multidocument summarization; 4.3. The DUC/TAC tasks for multidocument summarization and INEX Tweet Contextualization; 4.4. The taxonomy of multidocument summarization methods; 4.5. Some multi-document summarization systems and algorithms; 4.6. Update summarization; 4.7. Multi-document summarization by polytopes; 4.8. Redundancy; 4.9. Conclusion; 5 Multi and Cross-lingual Summarization. | |
505 | 8 | _a5.1. Multilingualism, the web and automatic summarization5.2. Automatic multilingual summarization; 5.3. MEAD; 5.4. SUMMARIST; 5.5. COLUMBIA NEWSBLASTER; 5.6. NEWSEXPLORER; 5.7. GOOGLE NEWS; 5.8. CAPS; 5.9. Automatic cross-lingual summarization; 5.10. Conclusion; 6 Source and Domain-Specific Summarization; 6.1. Genre, specialized documents and automatic summarization; 6.2. Automatic summarization and organic chemistry; 6.3. Automatic summarization and biomedicine; 6.4. Summarizing court decisions; 6.5. Opinion summarization; 6.6. Web summarization; 6.7. Conclusion; 7 Text Abstracting. | |
505 | 8 | _a7.1. Abstraction-based automatic summarization7.2. Systems using natural language generation; 7.3. An abstract generator using information extraction; 7.4. Guided summarization and a fully abstractive approach; 7.5. Abstraction-based summarization via conceptual graphs; 7.6. Multisentence fusion; 7.7. Sentence compression; 7.8. Conclusion; 8 Evaluating Document Summaries; 8.1. How can summaries be evaluated?; 8.2. Extrinsic evaluations; 8.3. Intrinsic evaluations; 8.4. TIPSTER SUMMAC evaluation campaigns; 8.5. NTCIR evaluation campaigns; 8.6. DUC/TAC evaluation campaigns. | |
650 | 0 | _aAutomatic abstracting. | |
650 | 7 |
_aLANGUAGE ARTS & DISCIPLINES _xLibrary & Information Science _xGeneral. _2bisacsh |
|
650 | 7 |
_aAutomatic abstracting. _2fast _0(OCoLC)fst00822691 |
|
655 | 4 | _aElectronic books. | |
776 | 0 | 8 |
_iPrint version: _z9781848216686 |
830 | 0 | _aCognitive science and knowledge management series. | |
856 | 4 | 0 |
_uhttp://onlinelibrary.wiley.com/book/10.1002/9781119004752 _zWiley Online Library |
938 |
_aBooks 24x7 _bB247 _nbks00063459 |
||
938 |
_aCoutts Information Services _bCOUT _n29855571 |
||
938 |
_aEBL - Ebook Library _bEBLB _nEBL1800890 |
||
938 |
_aEBL - Ebook Library _bEBLB _nEBL4040746 |
||
938 |
_aebrary _bEBRY _nebr10944998 |
||
938 |
_aEBSCOhost _bEBSC _n855994 |
||
938 |
_aIngram Digital eBook Collection _bIDEB _ncis29855571 |
||
938 |
_aRecorded Books, LLC _bRECE _nrbeEB00588428 |
||
938 |
_aYBP Library Services _bYANK _n12094758 |
||
938 |
_aYBP Library Services _bYANK _n12678062 |
||
938 |
_aYBP Library Services _bYANK _n12094498 |
||
994 |
_a92 _bDG1 |
||
999 |
_c13716 _d13716 |