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: A method is presented for the rhythmic pars<lb>ing problem: Given a sequence of observed<lb>musical note onset times, we simultaneously<lb>estimate the corresponding notated rhythm<lb>and tempo process. A graphical model is<lb>developed that<lb>represents the evolution of<lb>tempo and rhythm and relates these hid<lb>den quantities to an observable performance.<lb>The rhythm variables are discrete and the<lb>tempo and observation variables are contin<lb>uous. We show how to compute the<lb>glob<lb>ally most likely configuration of the tempo<lb>and rhythm variables given an observation of<lb>note onset times. Preliminary experiments<lb>are presented on a small data set. A gen<lb>eralization to computing MAP estimates for<lb>arbitrary conditional Gaussian distributions<lb>is outlined.
Solution:
A Mixed Graphical Model for Rhythmic Parsing