  The internet increased the amount of information available. However, the
reading and understanding of this information are costly tasks. In this
scenario, the Natural Language Processing (NLP) applications enable very
important solutions, highlighting the Automatic Text Summarization (ATS), which
produce a summary from one or more source texts. Automatically summarizing one
or more texts, however, is a complex task because of the difficulties inherent
to the analysis and generation of this summary. This master's thesis describes
the main techniques and methodologies (NLP and heuristics) to generate
summaries. We have also addressed and proposed some heuristics based on graphs
and similarity matrix to measure the relevance of judgments and to generate
summaries 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 were
quite interesting.
