Using Semantic Graphs and Word Sense Disambiguation Techniques to Improve Text Summarization

• Autores: Laura Plaza Morales , Alberto Díaz Esteban
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 47, 2011, págs. 97-105
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen
  • español

    En este trabajo se presenta un método para la generación automática de resúmenes basado en grafos semánticos. El sistema utiliza conceptos y relaciones de WordNet para construir un grafo que representa el documento, así como un algoritmo de clustering basado en la conectividad para descubrir los distintos temas tratados en él. La selección de oraciones para el resumen se realiza en función de la presencia en las oraciones de los conceptos más representativos del documento. Los experimentos realizados demuestran que el enfoque propuesto obtiene resultados significativamente mejores que otros sistemas evaluados bajo las mismas condiciones experimentales. Asimismo, el sistema puede ser fácilmente adaptado para trabajar con documentos de diferentes dominios, sin más que modificar la base de conocimiento y el método para identificar conceptos en el texto. Finalmente, este trabajo también estudia el efecto de la ambigüedad léxica en la generación de resúmenes.

  • English

    This paper presents a semantic graph-based method for extractive summarization. The summarizer uses WordNet concepts and relations to produce a semantic graph that represents the document, and a degree-based clustering algorithm is used to discover different themes or topics within the text. The selection of sentences for the summary is based on the presence in them of the most representative concepts for each topic. The method has proven to be an efficient approach to the identification of salient concepts and topics in free text. In a test on the DUC data for single document summarization, our system achieves significantly better results than previous approaches based on terms and mere syntactic information. Besides, the system can be easily ported to other domains, as it only requires modifying the knowledge base and the method for concept annotation. In addition, we address the problem of word ambiguity in semantic approaches to automatic summarization.

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