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We study a new content-based method for the evaluation of text summarization systems without human models which is used to produce system rankings. The research is carried out using a new content-based evaluation framework called FRESA to compute a variety of divergences among probability distributions. We apply our comparison framework to various well-established content-based evaluation measures in text summarization such as COVERAGE, RESPONSIVENESS, PYRAMIDS and ROUGE studying their associations in various text summarization tasks including generic multi-document summarization in English and French, focus-based multi-document summarization in English and generic single-document summarization in French and Spanish.

Abstract. In this paper, we propose an efficient strategy for summarizing scientific documents in Organic Chemistry that concentrates on numerical treatments. We present its implementation named yachs (Yet Another Chemistry Summarizer) that combines a specific document preprocessing with a sentence scoring method relying on the statistical properties of documents. We show that yachs achieves the best results among several other summarizers on a corpus made of Organic Chemistry articles.

International audienc

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