The Use of ART2 to create summaries from texts of different areas

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, pres...

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Detalles Bibliográficos
Autores principales: Christ, Rafael E. R., Bassani, T., Nievola, Julio César, Junior, Carlos N. Silla
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2004
Materias:
Ciencias Informáticas
ART2
Create Summaries
Texts of Different Areas
Neural nets
ARTIFICIAL INTELLIGENCE
Intelligent agents
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22561
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22561
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
ART2
Create Summaries
Texts of Different Areas
Neural nets
ARTIFICIAL INTELLIGENCE
Intelligent agents
spellingShingle Ciencias Informáticas
ART2
Create Summaries
Texts of Different Areas
Neural nets
ARTIFICIAL INTELLIGENCE
Intelligent agents
Christ, Rafael E. R.
Bassani, T.
Nievola, Julio César
Junior, Carlos N. Silla
The Use of ART2 to create summaries from texts of different areas
topic_facet Ciencias Informáticas
ART2
Create Summaries
Texts of Different Areas
Neural nets
ARTIFICIAL INTELLIGENCE
Intelligent agents
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.
format Objeto de conferencia
Objeto de conferencia
author Christ, Rafael E. R.
Bassani, T.
Nievola, Julio César
Junior, Carlos N. Silla
author_facet Christ, Rafael E. R.
Bassani, T.
Nievola, Julio César
Junior, Carlos N. Silla
author_sort Christ, Rafael E. R.
title The Use of ART2 to create summaries from texts of different areas
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 use of art2 to create summaries from texts of different areas
publishDate 2004
url http://sedici.unlp.edu.ar/handle/10915/22561
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