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: We introduce a novel latent vector space model that jointly learns<lb>the latent representations of words, e-commerce products and a<lb>mapping between the two without the need for explicit annotations.<lb>The power of the model lies in its ability to directly model the dis-<lb>criminative relation between products and a particular word. We<lb>compare our method to existing latent vector space models (LSI,<lb>LDA and word2vec) and evaluate it as a feature in a learning to<lb>rank setting. Our latent vector space model achieves its enhanced<lb>performance as it learns better product representations. Further-<lb>more, the mapping from words to products and the representations<lb>of words benefit directly from the errors propagated back from the<lb>product representations during parameter estimation. We provide<lb>an in-depth analysis of the performance of our model and analyze<lb>the structure of the learned representations.

Learning Latent Vector Spaces for Product Search