Combining graph connectivity and genetic clustering to improve biomedical summarization

Automatic summarization is emerging as a feasible instrument to help biomedical researchers to access online literature and face information overload. The Natural Language Processing community is actively working toward the development of effective summarization applications; however, automatic summaries are sometimes less informative than the user needs. In this work, our aim is to improve a summarization graph-based process combining genetic clustering with graph connectivity information. In this way, while genetic clustering allows us to identify the different topics that are dealt with in a document, connectivity information (in particular, degree centrality) allows us to asses and exploit the relevance of the different topics. Our automatic summaries are compared with others produced by commercial and research applications, to demonstrate the appropriateness of using this combination of techniques for automatic summarization.