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: Recent work on embedding ontology concepts has relied on either expensive manual annotation or automated concept tagging methods that ignore the textual contexts around concepts. We propose a novel method for jointly learning concept, phrase, and word embeddings from an unlabeled text corpus, by using the representative phrases for ontology concepts as distant supervision. We learn embeddings for medical concepts in the Unified Medical Language System and generaldomain concepts in YAGO, using a variety of corpora. Our embeddings show performance competitive with existing methods on concept similarity and relatedness tasks, while requiring no human corpus annotation and demonstrating more than 3x coverage in the vocabulary size.

A Weakly-Supervised Method for Jointly Embedding Concepts, Phrases, and Words