Automatic text summarization:
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London
ISTE [u.a.]
2014
|
| Edition: | 1. publ. |
| Series: | Cognitive science and knowledge management series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXIII, 348 S. graph. Darst. |
| ISBN: | 9781848216686 |
Staff View
MARC
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| 020 | |a 9781848216686 |c hbk. |9 978-1-84821-668-6 | ||
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| 245 | 1 | 0 | |a Automatic text summarization |c Juan-Manuel Torres-Moreno |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a London |b ISTE [u.a.] |c 2014 | |
| 300 | |a XXIII, 348 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Cognitive science and knowledge management series | |
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| 689 | 1 | |C b |5 DE-604 | |
| 700 | 1 | |a Torres-Moreno, Juan-Manuel |0 (DE-588)1064588662 |4 edt | |
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Record in the Search Index
| _version_ | 1804152772014112768 |
|---|---|
| adam_text | Contents
Foreword by
A. Zamora and
R.
Salvador
...... xi
Foreword by
H.
Saggion
.................. xv
Notation
............................. xvii
Introduction
.......................... xix
Part
1.
Foundations
..................... 1
Chapter
1.
Why Summarize Texts?
........... 3
1.1.
The need for automatic summarization
.......... 3
1.2.
Definitions of text summarization
............. 5
1.3.
Categorizing automatic summaries
............ 10
1.4.
Applications of automatic text summarization
...... 13
1.5.
About automatic text summarization
........... 15
1.6.
Conclusion
......................... 21
Chapter
2.
Automatic Text Summarization:
Some Important Concepts
................. 23
2.1.
Processes before the process
............... 23
2.1.1.
Sentence-term matrix: the vector space model
(VSM) model
...................... 26
2.2.
Extraction, abstraction or compression?
......... 28
vi
Automatic Text Summarization
2.3.
Extraction-based summarization
............. 30
2.3.1.
Surface-level algorithms
................ 31
2.3.2.
Intermediate-level algorithms
............. 33
2.3.3.
Deep parsing algorithms
................ 34
2.4.
Abstract summarization
.................. 35
2.4.1.
Frump
......................... 35
2.4.2.
Information extraction and abstract generation
... 38
2.5.
Sentence compression and Fusion
............ 38
2.5.1.
Sentence compression
................. 38
2.5.2.
Multisentence fusion
.................. 39
2.6.
The limits of extraction
.................. 39
2.6.1.
Cohesion and coherence
................ 40
2.6.2.
The HexTAC experiment
............... 42
2.7.
The evolution of text summarization tasks
........ 43
2.7.1.
Traditional tasks
.................... 43
2.7.2.
Current and future problems
............. 45
2.8.
Evaluating summaries
................... 50
2.9.
Conclusion
......................... 51
Chapter
3.
Single-document Summarization
... 53
3.1.
Historical approaches
................... 53
3.1.1.
Luhn s Automatic Creation of Literature Abstracts
. 57
3.1.2.
The Luhn algorithm
.................. 59
3.1.3.
Edmundson s linear combination
........... 61
3.1.4.
Extracts by elimination
................ 64
3.2.
Machine learning approaches
............... 66
3.2.1.
Machine learning parameters
............. 66
3.3.
State-of-the-art approaches
................ 69
3.4.
Latent semantic analysis
.................. 73
3.4.1.
Singular value decomposition
(SVD)
........ 73
3.4.2.
Sentence weighting by
SVD
............. 74
3.5.
Graph-based approaches
.................. 76
3.5.1.
PageR an
к
and SNA algorithms
........... 77
3.5.2.
Graphs and automatic text summarization
...... 78
3.5.3.
Constructing the graph
................. 79
3.5.4.
Sentence weighting
.................. 80
Contents
vii
3.6. DivTeX:
a summarizer
based on the divergence of
probability distribution
................... 83
3.7.
Cortex
........................... 85
3.7.1.
Frequenti ai
measures
................. 86
3.7.2.
Hamming measures
.................. 87
3.7.3.
Mixed measures
.................... 88
3.7.4.
Decision algorithm
................... 89
3.8.
Artex
............................ 90
3.9.
Enertex
.......................... 93
3.9.1.
Spins and neural networks
............... 93
3.9.2.
The textual energy similarity measure
........ 95
3.9.3.
Summarization by extraction and textual energy
. . 97
3.10.
Approaches using rhetorical analysis
.......... 102
3.11.
Lexical chains
....................... 107
3.12.
Conclusion
.........................
Î07
Chapter
4.
Guided Multi-Document
Summarization
......................... 109
4.1.
Introduction
......................... 109
4.2.
The problems of multidocument summarization
.... 110
4.3.
DUC/TAC
&
INEX Tweet
Contextuali
zation
...... 112
4.4.
The taxonomy of MDS methods
............. 115
4.4.1.
Structure based
..................... 115
4.4.2.
Vector space model based
............... 116
4.4.3.
Graph based
....................... 117
4.5.
Some
multi
-document summarization systems and
algorithms
.......................... 117
4.5.1.
Summons
....................... 118
4.5.2.
Maximal marginal relevance
............. 119
4.5.3.
A multidocument biography summarization system
120
4.5.4.
Multi-document Enertex
.............. 121
4.5.5.
Mead
.......................... 123
4.5.6.
Cats
.......................... 126
4.5.7.
SumUM and
ЅимМА
................ 128
4.5.8.
Neo-Cortex
..................... 131
4.6.
Update summarization
................... 134
4.6.1.
Update summarization pilot task at
DUC
2007 ... 134
viii Automatic
Text Summarization
4.6.2.
Update summarization task at
TAC
2008
and
2009 . 135
4.6.3.
A minimization-maximization approach
....... 138
4.6.4.
The ICSI system at
TAC
2008
and
2009....... 142
4.6.5.
The CBSEAS system at
TAC
............. 145
4.7.
Multidocument summarization by polytopes
...... 146
4.8.
Redundancy
......................... 148
4.9.
Conclusion
......................... 149
Part
2.
Emerging systems
................. 151
Chapter
5.
Multi
and Cross-lingual
Summarization
......................... 153
5.1.
Multilingualism, the web and automatic summarization
153
5.2.
Automatic multilingual summarization
.......... 156
5.3.
Mead
............................ 159
5.4.
Summarist
........................ 159
5.5.
Columbia NewsBlaster
............... 161
5.6.
NewsExplorer
..................... 163
5.7.
Google News
...................... 166
5.8.
Caps
............................. 166
5.9.
Automatic cross-lingual summarization
......... 168
5.9.1.
The quality of machine translation
.......... 169
5.9.2.
A graph-based cross-lingual summarizer
...... 172
5.10.
Conclusion
......................... 177
Chapter
6.
Source and Domain-Specific
Summarization
......................... 179
6.1.
Genre, specialized documents and automatic
summarization
....................... 179
6.2.
Automatic summarization and organic chemistry
.... 183
6.2.1.
YACHS2
......................... 183
6.3.
Automatic summarization and
biomedicine
....... 189
6.3.1.
SummTerm
...................... 189
6.3.2.
A linguistic-statistical approach
........... 196
6.4.
Summarizing court decisions
............... 201
6.5.
Opinion summarization
.................. 204
6.5.1.
CBSEAS at
TAC
2008
opinion task
......... 204
Contents
¡χ
6.6. Web
summarization
.................... 206
6.6.1.
Web page summarization
............... 206
6.6.2.
OCELOT and the statistical gist
........... 207
6.6.3.
Multitweet summarization
............... 211
6.6.4.
Email summarization
................. 215
6.7.
Conclusion
......................... 216
Chapter
7.
Text Abstracting
............... 219
7.1.
Abstraction-based automatic summarization
....... 219
7.2.
Systems using natural language generation
....... 220
7.3.
An abstract generator using information extraction
. . . 222
7.4.
Guided summarization and a fully abstractive approach
223
7.5.
Abstraction-based summarization via conceptual graphs
226
7.6.
Multisentence fusion
.................... 227
7.6.1.
Multisentence fusion via graphs
........... 228
7.6.2.
Graphs and keyphrase extraction: the
Takahe
system
.......................... 231
7.7.
Sentence compression
................... 232
7.7.1.
Symbolic approaches
................. 235
7.7.2.
Statistical approaches
................. 236
7.7.3.
A statistical-linguistic approach
........... 238
7.8.
Conclusion
......................... 241
Chapter
8.
Evaluating Document Summaries
. . . 243
8.1.
How can summaries be evaluated?
............ 243
8.2.
Extrinsic evaluations
.................... 245
8.3.
Intrinsic evaluations
.................... 246
8.3.1.
The baseline summary
................. 247
8.4.
Tipster
S
UMM
ас
evaluation campaigns
........ 248
8.4.1.
Ad hoc task
....................... 249
8.4.2.
Categorization task
................... 249
8.4.3.
Question-answering task
............... 250
8.5.
NTCIR evaluation campaigns
............... 250
8.6.
DUC/TAC evaluation campaigns
............. 251
8.6.1.
Manual evaluations
................... 252
8.7.
CLEF-INEX evaluation campaigns
............ 254
8.8.
Semi-automatic methods for evaluating summaries
. . 256
χ
Automatic
Text Summarization
8.8.1.
Level of granularity: the sentence
.......... 256
8.8.2.
Level of granularity: words
.............. 257
8.9.
Automatic evaluation via information theory
...... 263
8.9.1.
Divergence of probability distribution
........ 265
8.9.2.
Fresa
.......................... 266
8.10.
Conclusion
......................... 271
Conclusion
........................... 275
Appendix
1.
Information Retrieval,
NLP AND ATS
........................... 281
Appendix
2.
Automatic Text Summarization
Resources
............................ 305
Bibliography
.......................... 309
Index
................................ 343
|
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| discipline | Informatik |
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| id | DE-604.BV042235350 |
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| indexdate | 2024-07-10T01:16:01Z |
| institution | BVB |
| isbn | 9781848216686 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027673584 |
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| owner_facet | DE-19 DE-BY-UBM DE-355 DE-BY-UBR |
| physical | XXIII, 348 S. graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | ISTE [u.a.] |
| record_format | marc |
| series2 | Cognitive science and knowledge management series |
| spelling | Automatic text summarization Juan-Manuel Torres-Moreno 1. publ. London ISTE [u.a.] 2014 XXIII, 348 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Cognitive science and knowledge management series Text (DE-588)4059596-1 gnd rswk-swf Automation (DE-588)4003957-2 gnd rswk-swf Automatische Inhaltsanalyse (DE-588)4265353-8 gnd rswk-swf Zusammenfassung (DE-588)4224911-9 gnd rswk-swf Text (DE-588)4059596-1 s Zusammenfassung (DE-588)4224911-9 s Automation (DE-588)4003957-2 s DE-604 Automatische Inhaltsanalyse (DE-588)4265353-8 s b DE-604 Torres-Moreno, Juan-Manuel (DE-588)1064588662 edt Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027673584&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Automatic text summarization Text (DE-588)4059596-1 gnd Automation (DE-588)4003957-2 gnd Automatische Inhaltsanalyse (DE-588)4265353-8 gnd Zusammenfassung (DE-588)4224911-9 gnd |
| subject_GND | (DE-588)4059596-1 (DE-588)4003957-2 (DE-588)4265353-8 (DE-588)4224911-9 |
| title | Automatic text summarization |
| title_auth | Automatic text summarization |
| title_exact_search | Automatic text summarization |
| title_full | Automatic text summarization Juan-Manuel Torres-Moreno |
| title_fullStr | Automatic text summarization Juan-Manuel Torres-Moreno |
| title_full_unstemmed | Automatic text summarization Juan-Manuel Torres-Moreno |
| title_short | Automatic text summarization |
| title_sort | automatic text summarization |
| topic | Text (DE-588)4059596-1 gnd Automation (DE-588)4003957-2 gnd Automatische Inhaltsanalyse (DE-588)4265353-8 gnd Zusammenfassung (DE-588)4224911-9 gnd |
| topic_facet | Text Automation Automatische Inhaltsanalyse Zusammenfassung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027673584&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT torresmorenojuanmanuel automatictextsummarization |