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.
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.
Example solution: A Neural Local Coherence Model
Example 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: This paper develops a model that addresses syntactic embedding for machine comprehension, a key task of natural language understanding. Our proposed model, structural embedding of syntactic trees (SEST), takes each word in a sentence, constructs a sequence of syntactic nodes extracted from syntactic parse trees, and encodes the sequence into a vector representation. The learned vector is then incorporated into neural attention models, which allows learning the mapping of syntactic structures between question and context pairs. We evaluate our approach on SQuAD dataset and demonstrate that our model can accurately identify the syntactic boundaries of the sentences and to extract answers that are syntactically coherent over the baseline methods.
Solution: Structural Embedding of Syntactic Trees for Machine Comprehension