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.
One 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.
Solution is here: 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

Now, solve this: We provide several applications of Optimistic Mirror Descent, an online learning algorithm based on the idea<lb>of predictable sequences. First, we recover theMirror Prox algorithm for offline optimization, prove an extension<lb>to Holder-smooth functions, and apply the results to saddle-point type problems. Next, we prove that a version<lb>of Optimistic Mirror Descent (which has a close relation to the Exponential Weights algorithm) can be used by<lb>two strongly-uncoupled players in a finite zero-summatrix game to converge to the minimax equilibrium at the<lb>rate ofO((logT )/T ). This addresses a question of Daskalakis et al [6]. Further, we consider a partial information<lb>version of the problem. We then apply the results to convex programming and exhibit a simple algorithm for the<lb>approximateMax Flow problem.
Solution:
Optimization, Learning, and Games with Predictable Sequences