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.
Example: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Example solution: A Neural Local Coherence Model
Example explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage

Problem: Most of the existing image-to-image translation frameworksmapping an image in one domain to a corresponding image in anotherare based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework, which is based on variational autoencoders and generative adversarial networks. The proposed framework can learn the translation function without any corresponding images in two domains. We enable this learning capability by combining a weight-sharing constraint and an adversarial training objective. Through visualization results from various unsupervised image translation tasks, we verify the effectiveness of the proposed framework. An ablation study further reveals the critical design choices. Moreover, we apply the UNIT framework to the unsupervised domain adaptation task and achieve better results than competing algorithms do in benchmark datasets.
Solution: Unsupervised Image-to-Image Translation Networks