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 is below.
Q: 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.
A: A Neural Local Coherence Model
Rationale: 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
Q: We extend previous work on efficiently training linear models by applying stochastic updates to non-zero features only, lazily bringing weights current as needed. To date, only the closed form updates for the l1, l, and the rarely used l2 norm have been described. We extend this work by showing the proper closed form updates for the popular l22 and elastic net regularized models. We show a dynamic programming algorithm to calculate the proper elastic net update with only one constant-time subproblem computation per update. Our algorithm handles both fixed and decreasing learning rates and we derive the result for both stochastic gradient descent (SGD) and forward backward splitting (FoBoS). We empirically validate the algorithm, showing that on a bag-of-words dataset with 260, 941 features and 88 nonzero features on average per example, our method trains a logistic regression classifier with elastic net regularization 612 times faster than an otherwise identical implementation with dense updates.
A:
Efficient Elastic Net Regularization for Sparse Linear Models