Abstract
In this paper, we present a general framework for retrieving relevant information from news papers that exploits a novel summarization algorithm based on a deep semantic analysis of texts. In particular, we extract from each Web document a set of triples (subject, predicate, object) that are then used to build a summary through an unsupervised clustering algorithm exploiting the notion of semantic similarity. Finally, we leverage the centroids of clusters to determine the most significant summary sentences using some heuristics. Several experiments are carried out using the standard DUC methodology and ROUGE software and show how the proposed method outperforms several summarizer systems in terms of recall and readability.
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