As information is available in abundance for every topic on internet, condensing the
important information in the form of summary would benefit a number of users. Hence, there …

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 …

Creating impact in real-world settings requires artificial intelligence techniques to span the
full pipeline from data, to predictive models, to decisions. These components are typically …

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 treat the text summarization problem as maximizing a submodular function under a
budget constraint. We show, both theoretically and empirically, a modified greedy algorithm …

Text summarization is a process of extracting salient information from a source text and
presenting that information to the user in a condensed form while preserving its main …

Automatic text summarization schemes are indeed helpful for glancing briefly at the text
document. With this motivation, we introduce here a two-stage hybrid model for text …

This paper presents GFlow, a novel system for coherent extractive multi-document
summarization (MDS). 1 Where previous work on MDS considered sentence selection and …

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 …

We introduce a supervised model for predicting word importance that incorporates a rich set
of features. Our model is superior to prior approaches for identifying words used in human …