| Revue: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000379435 |
| ISSN: | 1405-5546 |
| Autores: | Litvak, Marina1 Vanetik, Natalia1 |
| Instituciones: | 1Shamoon College of Engineering, Beer-Sheva. Israel |
| Año: | 2014 |
| Periodo: | Jul-Sep |
| Volumen: | 18 |
| Número: | 3 |
| Paginación: | 581-589 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as possible, when compared to the original document set. We construct a new tensor-based representation that describes the given document set in terms of its topics. We then rank topics via Tensor Decomposition, and compile a summary from the sentences of the highest ranked topics |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Inteligencia artificial, Lingüística aplicada, Procesamiento de lenguaje natural, Minería de texto, Resumen de texto automático, Descomposición de tensor |
| Keyword: | Computer science, Literature and linguistics, Artificial intelligence, Applied linguistics, Natural language processing, Text mining, Automatic summarization, Tensor decomposition |
| Texte intégral: | Texto completo (Ver HTML) |
