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: The assignment of unique IDs to n variables is an instance of the well-known renaming problem, for which multiple algorithms have been proposed in the literature on distributed algorithms. However, to our knowledge, all these algorithms focus on robustness to failures, and ignore the issue of privacy. On the contrary, in this paper we do not consider agent failures, and we rather need an algorithm that protects agent and topology privacy. To this purpose, we propose Algorithm 1, which is a modification of the pseudo-tree generation algorithm in Online Appendix 2, and is an improved version of the algorithm proposed by Leaute and Faltings (2009). Each variable x is assigned a unique number idx that corresponds to the order in which it is first visited during the distributed traversal of the constraint graph (or, more precisely, an upper bound thereon). This is done by appending to each CHILD message the number id of variables visited so far (lines 8, 29 and 31). Each variable adds a random number to id so as not to leak any useful upper bound on its number of neighbors (lines 5 and 15). At the end of this algorithm, the root variable discovers an upper bound n+ on the total number of variables, and reveals it to everyone (lines 35 and 22 to 24).
Solution: Protecting Privacy through Distributed Computation in Multi-agent Decision Making Online Appendix 3: Unique ID Generation Algorithm