A Genetic Clustering Algorithm for Automatic Text Summarization
Abstract. Automatic text summarization has become a relevant topic due to the information overload. This automatization aims to help humans and machines to deal with the vast amount of text data (structured and un-structured) offered on the web and deep web. In this research a novel approach for aut...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2016 |
| País: | Colombia |
| Institución: | Universidad Nacional de Colombia |
| Repositorio: | Repositorio UN |
| Idioma: | español |
| OAI Identifier: | oai:repositorio.unal.edu.co:unal/57548 |
| Acceso en línea: | https://repositorio.unal.edu.co/handle/unal/57548 http://bdigital.unal.edu.co/53848/ |
| Access Level: | acceso abierto |
| Palabra clave: | 0 Generalidades / Computer science, information and general works 62 Ingeniería y operaciones afines / Engineering Text mining Genetic algorithm Clustering algorithm Automatic text summarization Single document automatic text summarization |
| Sumario: | Abstract. Automatic text summarization has become a relevant topic due to the information overload. This automatization aims to help humans and machines to deal with the vast amount of text data (structured and un-structured) offered on the web and deep web. In this research a novel approach for automatic extractive text summarization called SENCLUS is presented. Using a genetic clustering algorithm, SENCLUS clusters the sentences as close representation of the text topics using a fitness function based on redundancy and coverage, and applies a scoring function to select the most relevant sentences of each topic to be part of the extractive summary. The approach was validated using the DUC2002 data set and ROUGE summary quality measures. The results shows that the approach is representative against the state of the art methods for extractive automatic text summarization. |
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