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Generación de resúmenes extractivos de múltiples documentos usando grafos semánticos

  • Autores: José Ángel Olivas Varela Árbol académico, Francisco Pascual Romero Chicharro Árbol académico, Oleyda del Camino Valle, Alfredo J. Simón Cuevas, Eduardo Valladares Valdés
  • Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 63, 2019, págs. 103-110
  • Idioma: español
  • Títulos paralelos:
    • Multi-document extractive summarization using semantic graph
  • Enlaces
  • Resumen
    • español

      La generación automática de resúmenes consiste en sintetizar en un texto corto la información más relevante contenida en documentos, y permite reducir los problemas generados por la sobrecarga de información. En este trabajo se presenta un método no supervisado de generación de resúmenes extractivos a partir de múltiples documentos. En esta propuesta, la conceptualización y estructura semántica subyacente del contenido textual se representa en un grafo semántico usando WordNet y se aplica un algoritmo de agrupamiento de conceptos para identificar los tópicos tratados en los documentos, con los cuales se evalúa la relevancia de las oraciones para construir el resumen. El método fue evaluado con corpus de textos de MultiLing 2015, y se usaron métricas de ROUGE para medir la calidad de los resúmenes generados. Los resultados obtenidos se compararon con los de otros sistemas participantes en MultiLing 2015, evidenciándose mejoras en la mayoría de los casos. |

    • English

      The automatic texts summarization consists in synthesizing in a short text the most relevant information contained in text documents, and allows to reduce the generated problems by the information overload. In this paper, an unsupervised method for extractive multi-document summarization is presented. In this proposal, the conceptualization and underlying semantics structure of the textual content is represented in a semantic graph using WordNet, and a concept clustering algorithm is applied to identifying the topics of the documents set, with which the relevance of the sentences is evaluated to build the summary. The method was evaluated with texts corpus from MultiLing 2015, and ROUGE metrics were used to measure the quality of the generated summaries. The obtained results were compared with those other participant systems in MultiLing 2015, evidencing improves in most of the cases.

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