Definition: 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.
Input: Supervised (linear) embedding models like Wsabie [5] and PSI [1] have proven successful at ranking, recommendation and annotation tasks. However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature, and typically underfit. We propose a new class of models which aim to provide improved performance while retaining many of the benefits of the existing class of embedding models. Our new approach works by iteratively learning a linear embedding model where the next iteration’s features and labels are reweighted as a function of the previous iteration. We describe several variants of the family, and give some initial results. 1 (Supervised) Linear Embedding Models Standard linear embedding models are of the form: f(x, y) = xUV y = ∑

Affinity Weighted Embedding