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: Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. In this paper, we overcome this problem by using a linguisticallyenhanced alignment to automatically extract the edits between parallel original and corrected sentences and then classify them using a new dataset-independent rule-based classifier. As human experts rated the predicted error types as "Good" or "Acceptable" in at least 95% of cases, we applied our approach to the system output produced in the CoNLL-2014 shared task to carry out a detailed analysis of system error type performance for the first time.
Student:
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction