Calculating the Upper Bounds for Multi-Document Summarization using Genetic Algorithms



Título del documento: Calculating the Upper Bounds for Multi-Document Summarization using Genetic Algorithms
Revue: Computación y Sistemas
Base de datos: PERIÓDICA
Número de sistema: 000423275
ISSN: 1405-5546
Autores: 1
1
1
Instituciones: 1Universidad Autónoma del Estado de México, Unidad Académica Profesional Tianguistenco, Santiago Tianguistenco, Estado de México. México
Año:
Periodo: Ene-Mar
Volumen: 22
Número: 1
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Aplicado, descriptivo
Resumen en inglés Over the last years, several Multi-Document Summarization (MDS) methods have been presented in Document Understanding Conference (DUC), workshops. Since DUC01, several methods have been presented in approximately 268 publications of the state-of-the-art, that have allowed the continuous improvement of MDS, however in most works the upper bounds were unknowns. Recently, some works have been focused to calculate the best sentence combinations of a set of documents and in previous works we have been calculated the significance for single-document summarization task in DUC01 and DUC02 datasets. However, for MDS task has not performed an analysis of significance to rank the best multi-document summarization methods. In this paper, we describe a Genetic Algorithm-based method for calculating the best sentence combinations of DUC01 and DUC02 datasets in MDS through a Meta-document representation. Moreover, we have calculated three heuristics mentioned in several works of state-of-the-art to rank the most recent MDS methods, through the calculus of upper bounds and lower bounds
Disciplinas: Bibliotecología y ciencia de la información,
Ciencias de la computación
Palabras clave: Análisis y sistematización de la información,
Procesamiento de lenguaje natural,
Encabezamientos,
Integración de documentos,
Algoritmos genéticos,
Significancia
Keyword: Information analysis,
Natural language processing,
Toplines,
Document summarization,
Genetic algorithms,
Significance
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