We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy---e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. Technically, we consider an ever-growing table of objects with dimension and measure attributes. A situational fact is a "contextual" skyline tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes, when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual skyline tuple, and discover them quickly before the event becomes yesterday's news. A brute-force approach requires exhaustive comparison with every tuple, under every constraint, and in every measure subspace. We design algorithms in response to these challenges using three corresponding ideas---tuple reduction, constraint pruning, and sharing computation across measure subspaces. We also adopt a simple prominence measure to rank the discovered facts when they are numerous. Experiments over two real datasets validate the effectiveness and efficiency of our techniques.