Detailed Instructions: 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.
See one example below:
Problem: 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
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: Classical Decision Theory provides a norma tive framework for representing and reason ing about complex preferences. Straightfor ward application of this theory to automate decision making is difficult due to high elic itation cost. In response to this problem, researchers have recently developed a num ber of qualitative, logic-oriented approaches for representing and reasoning about pref erences. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to pref erence elicitation and decision making that is grounded in class ical multi-attribute util ity theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about class es of deci sion alternatives can be used to further con strain the utility function and thus identify sup-optimal alternatives. This work demon strates that quantitative and qualitative ap proaches can be synergistically integrated to provide effective and flexible decision sup port.
Solution:
A Hybrid Approach to Reasoning with Partially Elicited Preference Models