You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
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.

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.
Solution: A Neural Local Coherence Model
Why? 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: We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current mod els that combine both primitive actions and macro-actions and leave the state space un changed, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macro actions as local policies that act in certain regions of state space, and by restricting states in the ab stract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss sev eral ways in which macro-actions can be gen erated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational over head of macro-action generation.
Solution:
Hierarchical Solution of Markov Decision Processes using Macro-actions