  Automatic Text Summarization strategies have been successfully employed to
digest text collections and extract its essential content. Usually, summaries
are generated using textual corpora that belongs to the same domain area where
the summary will be used. Nonetheless, there are special cases where it is not
found enough textual sources, and one possible alternative is to generate a
summary from a different domain. One manner to summarize texts consists of
using a graph model. This model allows giving more importance to words
corresponding to the main concepts from the target domain found in the
summarized text. This gives the reader an overview of the main text concepts as
well as their relationships. However, this kind of summarization presents a
significant number of repeated terms when compared to human-generated
summaries. In this paper, we present an approach to produce graph-model
extractive summaries of texts, meeting the target domain exigences and treating
the terms repetition problem. To evaluate the proposition, we performed a
series of experiments showing that the proposed approach statistically improves
the performance of a model based on Graph Centrality, achieving better
coverage, accuracy, and recall.
