Given the task definition, example input & output, solve the new input case.
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.
Output: A Neural Local Coherence Model
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

New input case for you: Transferring knowledge across a sequence of related tasks is an important challenge in reinforce-<lb>ment learning. Despite much encouraging empirical evidence that shows benefits of transfer, there<lb>has been very little theoretical analysis. In this paper, we study a class of lifelong reinforcement-<lb>learning problems: the agent solves a sequence of tasks modeled as finite Markov decision processes<lb>(MDPs), each of which is from a finite set of MDPs with the same state/action spaces and different<lb>transition/reward functions. Inspired by the need for cross-task exploration in lifelong learning, we<lb>formulate a novel online discovery problem and give an optimal learning algorithm to solve it. Such<lb>results allow us to develop a new lifelong reinforcement-learning algorithm, whose overall sample<lb>complexity in a sequence of tasks is much smaller than that of single-task learning, with high prob-<lb>ability, even if the sequence of tasks is generated by an adversary. Benefits of the algorithm are<lb>demonstrated in a simulated problem.
Output:
The Online Discovery Problem and Its Application to Lifelong Reinforcement Learning