  We survey multi-label ranking tasks, specifically multi-label classification
and label ranking classification. We highlight the unique challenges, and
re-categorize the methods, as they no longer fit into the traditional
categories of transformation and adaptation. We survey developments in the last
demi-decade, with a special focus on state-of-the-art methods in deep learning
multi-label mining, extreme multi-label classification and label ranking. We
conclude by offering a few future research directions.
