We present an empirically grounded method for evaluating content selection in summarization. It incorporates the idea that no single best model summary for a collection of documen...
We present a fully automatic method for content selection evaluation in summarization that does not require the creation of human model summaries. Our work capitalizes on the assu...
This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a queryfocused summary. Several SVMs are trained using information fr...
In the field of multi-document summarization, the Pyramid method has become an important approach for evaluating machine-generated summaries. The method is based on the manual ann...
Leonhard Hennig, Ernesto William De Luca, Sahin Al...
Pyramid annotation makes it possible to evaluate quantitatively and qualitatively the content of machine-generated (or human) summaries. Evaluation methods must prove themselves a...