  In automatic summarization, centrality-as-relevance means that the most
important content of an information source, or a collection of information
sources, corresponds to the most central passages, considering a representation
where such notion makes sense (graph, spatial, etc.). We assess the main
paradigms, and introduce a new centrality-based relevance model for automatic
summarization that relies on the use of support sets to better estimate the
relevant content. Geometric proximity is used to compute semantic relatedness.
Centrality (relevance) is determined by considering the whole input source (and
not only local information), and by taking into account the existence of minor
topics or lateral subjects in the information sources to be summarized. The
method consists in creating, for each passage of the input source, a support
set consisting only of the most semantically related passages. Then, the
determination of the most relevant content is achieved by selecting the
passages that occur in the largest number of support sets. This model produces
extractive summaries that are generic, and language- and domain-independent.
Thorough automatic evaluation shows that the method achieves state-of-the-art
performance, both in written text, and automatically transcribed speech
summarization, including when compared to considerably more complex approaches.
