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.

Let me give you an 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.
The answer to this example can be: A Neural Local Coherence Model
Here is why: 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

OK. solve this:
Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced in a number of interdisciplinary areas such as computational biology and drug design. Typically, kernel functions are designed beforehand for a data type which either exploit statistics of the structures or make use of probabilistic generative models, and then a discriminative classifier is learned based on the kernels via convex optimization. However, such an elegant two-stage approach also limited kernel methods from scaling up to millions of data points, and exploiting discriminative information to learn feature representations. We propose an effective and scalable approach for structured data representation which is based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information. Furthermore, our feature learning algorithm runs a sequence of function mappings in a way similar to graphical model inference procedures, such as mean field and belief propagation. In real world applications involving sequences and graphs, we showed that the proposed approach is much more scalable than alternatives while at the same time produce comparable results to the state-of-the-art in terms of classification and regression.
Answer:
Discriminative Embeddings of Latent Variable Models for Structured Data