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: To improve user satisfaction, mobile app developers are interested in relevant user opinions such as complaints or suggestions. An important source for such opinions is user reviews on online app markets. However, manual review analysis for useful opinions is often challenging due to the large amount and the noisy-nature of user reviews. To address this problem, we propose M.A.R.K, a keyword-based framework for semiautomated review analysis. The key task of M.A.R.K is to analyze reviews for keywords of potential interest which developers can use to search for useful opinions. We have developed several techniques for that task including: 1) keyword extracting with customized regularization algorithms; 2) keyword grouping with distributed representation; and 3) keyword ranking with ratings and frequencies analysis. Our empirical evaluation and case studies show that M.A.R.K can identify keywords of high interest and provide developers with useful user opinions. KeywordsApp Review, Opinion Mining, Keyword
Solution:
Mining User Opinions in Mobile App Reviews: A Keyword-based Approach