The Use of ART2 to create summaries from texts of different areas
- Autores
- Christ, Rafael E. R.; Bassani, T.; Nievola, Julio César; Junior, Carlos N. Silla
- Año de publicación
- 2004
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The volume of documents available electronically is growing fast, so it becomes difficult to access and select desired information in a fast and efficient way. In this context the automatic summarization task assumes a very imperative role; therefore one seeks to reduce the size of a document, preserving to the maximum its informative content. In this paper, it’s applied a model which uses sentence clusters from an ART2 neural network to generate extractive summaries. Different models can be developed from distinct area documents. Hence, the aim of this work is to evaluate the performance of those models when they summarize documents from correlated or non correlated areas.
Eje: V - Workshop de agentes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
ART2
Create Summaries
Texts of Different Areas
Neural nets
ARTIFICIAL INTELLIGENCE
Intelligent agents - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22561
Ver los metadatos del registro completo
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The Use of ART2 to create summaries from texts of different areasChrist, Rafael E. R.Bassani, T.Nievola, Julio CésarJunior, Carlos N. SillaCiencias InformáticasART2Create SummariesTexts of Different AreasNeural netsARTIFICIAL INTELLIGENCEIntelligent agentsThe volume of documents available electronically is growing fast, so it becomes difficult to access and select desired information in a fast and efficient way. In this context the automatic summarization task assumes a very imperative role; therefore one seeks to reduce the size of a document, preserving to the maximum its informative content. In this paper, it’s applied a model which uses sentence clusters from an ART2 neural network to generate extractive summaries. Different models can be developed from distinct area documents. Hence, the aim of this work is to evaluate the performance of those models when they summarize documents from correlated or non correlated areas.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2004-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22561enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:36:37Zoai:sedici.unlp.edu.ar:10915/22561Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:36:38.178SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
The Use of ART2 to create summaries from texts of different areas |
| title |
The Use of ART2 to create summaries from texts of different areas |
| spellingShingle |
The Use of ART2 to create summaries from texts of different areas Christ, Rafael E. R. Ciencias Informáticas ART2 Create Summaries Texts of Different Areas Neural nets ARTIFICIAL INTELLIGENCE Intelligent agents |
| title_short |
The Use of ART2 to create summaries from texts of different areas |
| title_full |
The Use of ART2 to create summaries from texts of different areas |
| title_fullStr |
The Use of ART2 to create summaries from texts of different areas |
| title_full_unstemmed |
The Use of ART2 to create summaries from texts of different areas |
| title_sort |
The Use of ART2 to create summaries from texts of different areas |
| dc.creator.none.fl_str_mv |
Christ, Rafael E. R. Bassani, T. Nievola, Julio César Junior, Carlos N. Silla |
| author |
Christ, Rafael E. R. |
| author_facet |
Christ, Rafael E. R. Bassani, T. Nievola, Julio César Junior, Carlos N. Silla |
| author_role |
author |
| author2 |
Bassani, T. Nievola, Julio César Junior, Carlos N. Silla |
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author author author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas ART2 Create Summaries Texts of Different Areas Neural nets ARTIFICIAL INTELLIGENCE Intelligent agents |
| topic |
Ciencias Informáticas ART2 Create Summaries Texts of Different Areas Neural nets ARTIFICIAL INTELLIGENCE Intelligent agents |
| dc.description.none.fl_txt_mv |
The volume of documents available electronically is growing fast, so it becomes difficult to access and select desired information in a fast and efficient way. In this context the automatic summarization task assumes a very imperative role; therefore one seeks to reduce the size of a document, preserving to the maximum its informative content. In this paper, it’s applied a model which uses sentence clusters from an ART2 neural network to generate extractive summaries. Different models can be developed from distinct area documents. Hence, the aim of this work is to evaluate the performance of those models when they summarize documents from correlated or non correlated areas. Eje: V - Workshop de agentes y sistemas inteligentes Red de Universidades con Carreras en Informática (RedUNCI) |
| description |
The volume of documents available electronically is growing fast, so it becomes difficult to access and select desired information in a fast and efficient way. In this context the automatic summarization task assumes a very imperative role; therefore one seeks to reduce the size of a document, preserving to the maximum its informative content. In this paper, it’s applied a model which uses sentence clusters from an ART2 neural network to generate extractive summaries. Different models can be developed from distinct area documents. Hence, the aim of this work is to evaluate the performance of those models when they summarize documents from correlated or non correlated areas. |
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2004 |
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2004-10 |
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