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 propose Novel Object Captioner (NOC), a deep visual semantic captioning model that can describe a large number of object categories not present in existing image-caption datasets. Recent captioning models are limited in their ability to scale and describe concepts outside of paired image-text corpora. Our model takes advantage of external sources labeled images from object recognition datasets, and semantic knowledge extracted from unannotated text and combines them to generate descriptions about novel objects. We propose minimizing a joint objective which can learn from diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of imagecaption datasets. We demonstrate that our model exploits semantic information to generate captions for hundreds of object categories in the ImageNet object recognition dataset that are not observed in imagecaption training data, as well as many categories that are observed very rarely.

Captioning Images with Diverse Objects