Discovering Diverse and Salient Threads in Document Collections

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How researchers see this paper

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Determinantal Point Processes for Machine Learning (2012, methodology): “6 Experiments: Threading Graphs In this section we put together many of the techniques introduced in this monograph in order to complete a novel task that we refer to as graph threading [57].”
Recent advances in document summarization (2017, methodology): “They employ hierarchical nonparametric Bayesian model to infer hierarchical relations among topics and enhance the diversity of themes by using structured determinantal point processes [61].”
Near-Optimal MAP Inference for Determinantal Point Processes (2012, methodology): “Recently, probabilistic models extending determinantal point processes (DPPs) [8, 9] were proposed for several such problems [10, 5, 11].”
Diverse Sequential Subset Selection for Supervised Video Summarization (2014, methodology): “The determinantal point process (DPP) has recently emerged as a powerful method for selecting a diverse subset from a “ground set” of items [13], with applications including document summarization [14] and information retrieval [15].”
A Systematic Exploration of Diversity in Machine Translation (2013, methodology): “DPPs have been applied to summarization (Kulesza and Taskar, 2011) and discovery of topical threads in document collections (Gillenwater et al., 2012).”