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: We propose an online convex optimization algorithm (RESCALEDEXP) that achieves<lb>optimal regret in the unconstrained setting without prior knowledge of any bounds<lb>on the loss functions. We prove a lower bound showing an exponential sep-<lb>aration between the regret of existing algorithms that require a known bound<lb>on the loss functions and any algorithm that does not require such knowledge.<lb>RESCALEDEXP matches this lower bound asymptotically in the number of itera-<lb>tions. RESCALEDEXP is naturally hyperparameter-free and we demonstrate empir-<lb>ically that it matches prior optimization algorithms that require hyperparameter<lb>optimization. 1 Online Convex Optimization Online Convex Optimization (OCO) [1, 2] provides an elegant framework for modeling noisy,<lb>antagonistic or changing environments. The problem can be stated formally with the help of the<lb>following definitions:<lb>Convex Set: A setW is convex ifW is contained in some real vector space and tw+(1 t)w W<lb>for all w,w W and t [0, 1].<lb>Convex Function: f :W R is a convex function if f(tw + (1 t)w) tf(w) + (1 t)f(w)<lb>for all w,w W and t [0, 1]. An OCO problem is a game of repeated rounds in which on round t a learner first chooses an element<lb>wt in some convex space W , then receives a convex loss function 't, and suffers loss 't(wt). The<lb>regret of the learner with respect to some other u W is defined by
Solution:
Online Convex Optimization with Unconstrained Domains and Losses