Extractive text summarization: can we use the same techniques for any text?

In this paper we address two issues. The first one analyzes whether the performance of a text summarization method depends on the topic of a document. The second one is concerned with how certain linguistic properties of a text may affect the performance of a number of automatic text summarization methods. For this we consider semantic analysis methods, such as textual entailment and anaphora resolution, and we study how they are related to proper noun, pronoun and noun ratios calculated over original documents that are grouped into related topics. Given the obtained results, we can conclude that although our first hypothesis is not supported, since it has been found no evident relationship between the topic of a document and the performance of the methods employed, adapting summarization systems to the linguistic properties of input documents benefits the process of summarization.

This research work has been partially funded by the European Commission under the Seventh (FP7 - 2007-2013) Framework Programme for Research and Technological Development through the FIRST project (FP7-287607); the Spanish Government through the project TEXTMESS 2.0 (TIN2009-13391-C04), ”Análisis de Tendencias Mediante Técnicas de Opinión Semántica” (TIN2012-38536-C03-03 ) and “Técnicas de Deconstrucción en la Tecnologías del Lenguaje Humano” (TIN2012-31224); and by the Valencian Government through the project PROMETEO (PROMETEO/2009/199).