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: <lb>The minimization of the logistic loss is a popular approach to batch supervised learning. Our<lb>paper starts from the surprising observation that, when fitting linear (or kernelized) classifiers,<lb>the minimization of the logistic loss is equivalent to the minimization of an exponential rado-loss<lb>computed (i) over transformed data that we call Rademacher observations (rados), and (ii) over<lb>the same classifier as the one of the logistic loss. Thus, a classifier learnt from rados can be<lb>directly used to classify observations. We provide a learning algorithm over rados with boosting-<lb>compliant convergence rates on the logistic loss (computed over examples). Experiments on<lb>domains with up to millions of examples, backed up by theoretical arguments, display that<lb>learning over a small set of random rados can challenge the state of the art that learns over<lb>the complete set of examples. We show that rados comply with various privacy requirements<lb>that make them good candidates for machine learning in a privacy framework. We give several<lb>algebraic, geometric and computational hardness results on reconstructing examples from rados.<lb>We also show how it is possible to craft, and efficiently learn from, rados in a differential privacy<lb>framework. Tests reveal that learning from differentially private rados can compete with learning<lb>from random rados, and hence with batch learning from examples, achieving non-trivial privacy<lb>vs accuracy tradeoffs.
Solution:
Rademacher Observations, Private Data, and Boosting