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: Topic models provide a useful method for dimensionality reduction and exploratory data<lb>analysis in large text corpora. Most approaches to topic model inference have been based on<lb>a maximum likelihood objective. Efficient algorithms exist that approximate this objective,<lb>but they have no provable guarantees. Recently, algorithms have been introduced that provide<lb>provable bounds, but these algorithms are not practical because they are inefficient and not ro-<lb>bust to violations of model assumptions. In this paper we present an algorithm for topic model<lb>inference that is both provable and practical. The algorithm produces results comparable to the<lb>best MCMC implementations while running orders of magnitude faster.
Solution: A Practical Algorithm for Topic Modeling with Provable Guarantees