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 input: 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 output: 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
Q: We introduce the value iteration network: a fully differentiable neural network with a 'planning module' embedded within. Value iteration networks are suitable for making predictions about outcomes that involve planning-based reasoning, such as predicting a desired trajectory from an observation of a map. Key to our approach is a novel differentiable approximation of the valueiteration algorithm, which can be represented as a convolutional neural network, and trained endto-end using standard backpropagation. We evaluate our value iteration networks on the task of predicting optimal obstacle-avoiding trajectories from an image of a landscape, both on synthetic data, and on challenging raw images of the Mars terrain.
A:
Value Iteration Networks