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: Recent years have seen the development of a number<lb>of methods for multiagent planning under uncertainty<lb>that scale to tens or even hundreds of agents. However,<lb>most of these methods either make restrictive assump-<lb>tions on the problem domain, or provide approximate<lb>solutions without any guarantees on quality. To allow<lb>for meaningful benchmarking through measurable qual-<lb>ity guarantees on a very general class of problems, this<lb>paper introduces a family of influence-optimistic upper<lb>bounds for factored Dec-POMDPs. Intuitively, we de-<lb>rive bounds on very large multiagent planning problems<lb>by subdividing them in sub-problems, and at each of<lb>these sub-problems making optimistic assumptions with<lb>respect to the influence that will be exerted by the rest<lb>of the system. We numerically compare the different<lb>upper bounds and demonstrate how, for the first time<lb>ever, we can achieve a non-trivial guarantee that the<lb>heuristic solution of problems with hundreds of agents<lb>is close to optimal. Furthermore, we provide evidence<lb>that the upper bounds may improve the effectiveness of<lb>heuristic influence search, and discuss further potential<lb>applications to multiagent planning.
Output:
Influence-Optimistic Local Values for Multiagent Planning Extended Version