With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

We design a class of submodular functions meant for document summarization tasks. These
functions each combine two terms, one which encourages the summary to be representative …

We address the challenge of automatically generating questions from reading materials for
educational practice and assessment. Our approach is to overgenerate questions, then rank …

We present an exploration of generative probabilistic models for multi-document
summarization. Beginning with a simple word frequency based model (Nenkova and …

Is it possible to develop an" AI Pathologist" to pass the board-certified examination of the
American Board of Pathology? To achieve this goal, the first step is to create a visual …

Texts with potential educational value are becoming available through the Internet (eg,
Wikipedia, news services). However, using these new tests in classrooms introduces many …

Abstract We present an Integer Linear Program for exact inference under a maximum
coverage model for automatic summarization. We compare our model, which operates at the …

We present a new approach to automatic summarization based on neural nets, called
NetSum. We extract a set of features from each sentence that helps identify its importance in …

Most existing research on applying machine learning techniques to document
summarization explores either classification models or learning-to-rank models. This paper …

We introduce a method to learn a mixture of submodular" shells" in a large-margin setting. A
submodular shell is an abstract submodular function that can be instantiated with a ground …