  The volume of news content has increased significantly in recent years and
systems to process and deliver this information in an automated fashion at
scale are becoming increasingly prevalent. One critical component that is
required in such systems is a method to automatically determine how notable a
certain news story is, in order to prioritize these stories during delivery.
One way to do so is to compare each story in a stream of news stories to a
notable event. In other words, the problem of detecting notable news can be
defined as a ranking task; given a trusted source of notable events and a
stream of candidate news stories, we aim to answer the question: "Which of the
candidate news stories is most similar to the notable one?". We employ
different combinations of features and learning to rank (LTR) models and gather
relevance labels using crowdsourcing. In our approach, we use structured
representations of candidate news stories (triples) and we link them to
corresponding entities. Our evaluation shows that the features in our proposed
method outperform standard ranking methods, and that the trained model
generalizes well to unseen news stories.
