Teacher: 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.
Teacher: Now, understand the problem? If you are still confused, see the following 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.
Solution: A Neural Local Coherence Model
Reason: 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

Now, solve this instance: Named Entity Recognition (NER) is a key NLP task, which is all the more chal-<lb>lenging on Web and user-generated content with their diverse and continuously<lb>changing language. This paper aims to quantify how this diversity impacts<lb>state-of-the-art NER methods, by measuring named entity (NE) and context<lb>variability, feature sparsity, and their effects on precision and recall. In particu-<lb>lar, our findings indicate that NER approaches struggle to generalise in diverse<lb>genres with limited training data. Unseen NEs, in particular, play an impor-<lb>tant role, which have a higher incidence in diverse genres such as social media<lb>than in more regular genres such as newswire. Coupled with a higher incidence<lb>of unseen features more generally and the lack of large training corpora, this<lb>leads to significantly lower F1 scores for diverse genres as compared to more<lb>regular ones. We also find that leading systems rely heavily on surface forms<lb>found in training data, having problems generalising beyond these, and offer<lb>explanations for this observation.
Student:
Generalisation in Named Entity Recognition: A Quantitative Analysis