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: While influence diagrams have many ad vantages as a representation framework for Bayesian decision problems, they have a se rious drawback in handling asymmetric de cision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A consid erable amount of unnecessary computation may be involved when a symmetrized influ ence diagram is evaluated by conventional al gorithms. In this paper we present an ap proach for avoiding such unnecessary compu tation in influence diagram evaluation.
Solution: Solving Asymmetric Decision Problems with Influence Diagrams