In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Q: To cope with the high level of ambiguity faced in domains such as Computer<lb>Vision or Natural Language processing, robust prediction methods often search<lb>for a diverse set of high-quality candidate solutions or proposals. In structured<lb>prediction problems, this becomes a daunting task, as the solution space (image<lb>labelings, sentence parses, etc.) is exponentially large. We study greedy algo-<lb>rithms for finding a diverse subset of solutions in structured-output spaces by<lb>drawing new connections between submodular functions over combinatorial item<lb>sets and High-Order Potentials (HOPs) studied for graphical models. Specifically,<lb>we show via examples that when marginal gains of submodular diversity functions<lb>allow structured representations, this enables efficient (sub-linear time) approxi-<lb>mate maximization by reducing the greedy augmentation step to inference in a<lb>factor graph with appropriately constructed HOPs. We discuss benefits, trade-<lb>offs, and show that our constructions lead to significantly better proposals.
A:
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets