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: Systematic use of the published results of randomized clinical trials is<lb>increasingly important in evidence-based medicine. In order to collate and<lb>analyze the results from potentially numerous trials, evidence tables are<lb>used to represent trials concerning a set of interventions of interest. An<lb>evidence table has columns for the patient group, for each of the interven-<lb>tions being compared, for the criterion for the comparison (e.g. proportion<lb>who survived after 5 years from treatment), and for each of the results.<lb>Currently, it is a labour-intensive activity to read each published paper<lb>and extract the information for each field in an evidence table. There have<lb>been some NLP studies investigating how some of the features from papers<lb>can be extracted, or at least the relevant sentences identified. However,<lb>there is a lack of an NLP system for the systematic extraction of each item<lb>of information required for an evidence table. We address this need by a<lb>combination of a maximum entropy classifier, and integer linear program-<lb>ming. We use the later to handle constraints on what is an acceptable<lb>classification of the features to be extracted. With experimental results,<lb>we demonstrate substantial advantages in using global constraints (such<lb>as the features describing the patient group, and the interventions, must<lb>occur before the features describing the results of the comparison).
Student:
Extraction of evidence tables from abstracts of randomized clinical trials using a maximum entropy classifier and global constraints