A framework for obtaining structurally complex condensed representations of document sets in the biomedical domain

En este art culo presentamos un marco para la obtenci on de representa- ciones condensadas estructuralmente complejas de conjuntos de documentos, el cual servir a de base para la construcci on de res umenes, la obtenci on de respuestas para preguntas complejas, etc. Este marco incluye un m etodo para extraer una lista ordenada de hechos, triplos de la forma entidad - relaci on - entidad, el cual usa patrones de extracci on basados en an alisis de dependencias y modelos de lenguajes; y m etodos para construir un grafo bipartito que codi que la informaci on contenida en el conjunto de hechos y determinar un orden de recorrido apropiado sobre dicha estructura. Evaluamos los componentes de nuestro marco sobre una subcolecci on extra da de MEDLINE. Los resultados obtenidos son prometedores.In this paper, we present a framework for obtaining structurally complex condensed representations of documents sets, which will be used as a base for sum- marization, answering complex questions, etc. This framework includes a method for extracting a ranked list of facts, triples of the form entity - relation - entity, which relies on dependency parsing-based extraction patterns and language modeling; and methods for constructing a bipartite graph encoding the information contained in the set of facts and determining an appropriate traversing order on that structure. We evaluate the components of our framework on a subcollection extracted from MEDLINE, obtaining promising results

Text summarization in the biomedical domain: A systematic review of recent research

The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain