You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
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.

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
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

New input: Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Processthe multi-level clustering hierarchical Dirichlet Process (MLC-HDP)that simultaneously clusters datasets on multiple levels. Our seizure dataset contains brain activity recorded in typically more than a hundred individual channels for each seizure of each patient. The MLC-HDP model clusters over channels-types, seizure-types, and patient-types simultaneously. We describe this model and its implementation in detail. We also present the results of a simulation study comparing the MLC-HDP to a similar model, the Nested Dirichlet Process and finally demonstrate the MLC-HDP's use in modeling seizures across multiple patients. We find the MLC-HDP's clustering to be comparable to independent human physician clusterings. To our knowledge, the MLCHDP model is the first in the epilepsy literature capable of clustering seizures within and between patients.
Solution:
A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling