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: Natural language generation plays a critical role in any spoken dialogue system. We present a new approach to natural language generation using recurrent neural networks in an encoderdecoder framework. In contrast with previous work, our model uses both lexicalized and delexicalized versions of slot-value pairs for each dialogue act. This allows our model to learn from all available data, rather than being restricted to learning only from delexicalized slot-value pairs. We show that this helps our model generate more natural sentences with better grammar. We further improve our model's performance by initializing its weights from a pretrained language model. Human evaluation of our best-performing model indicates that it generates sentences which users find more natural and appealing.
Solution: Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data