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The internet increased the amount of information available. However, thereading and understanding of this information are costly tasks. In thisscenario, the Natural Language Processing (NLP) applications enable veryimportant solutions, highlighting the Automatic Text Summarization (ATS), whichproduce a summary from one or more source texts. Automatically summarizing oneor more texts, however, is a complex task because of the difficulties inherentto the analysis and generation of this summary. This master's thesis describesthe main techniques and methodologies (NLP and heuristics) to generatesummaries. We have also addressed and proposed some heuristics based on graphsand similarity matrix to measure the relevance of judgments and to generatesummaries by extracting sentences. We used the multiple languages (English,French and Spanish), CSTNews (Brazilian Portuguese), RPM (French) and DECODA(French) corpus to evaluate the developped systems. The results obtained werequite interesting.

Utiliza\c{c}\~ao de Grafos e Matriz de Similaridade na Sumariza\c{c}\~ao Autom\'atica de Documentos Baseada em Extra\c{c}\~ao de Frases