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.

Let me give you an 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.
The answer to this example can be: A Neural Local Coherence Model
Here is why: 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

OK. solve this:
Recent advances have made it feasible to apply the stochastic variational paradigm to a collapsed representation of latent Dirichlet allocation (LDA). While the stochastic variational paradigm has successfully been applied to an uncollapsed representation of the hierarchical Dirichlet process (HDP), no attempts to apply this type of inference in a collapsed setting of non-parametric topic modeling have been put forward so far. In this paper we explore such a collapsed stochastic variational Bayes inference for the HDP. The proposed online algorithm is easy to implement and accounts for the inference of hyper-parameters. First experiments show a promising improvement in predictive performance. 1 Background We begin by considering a model where each document d is a mixture d of K discrete topicdistributions k over a vocabulary of V terms. Let zdi {1, ..,K} denote the topic of the i word wdi {1, .., V } in document d {1, .., D} and place Dirichlet priors on the parameters d, k. We have zdi | d Discrete(d) , d Dirichlet() , wdi | zdi, {k} Discrete(zdi) , k Dirichlet() , where is the top-level distribution over topics, and and are concentration parameters. While the dimensionality of K is fixed in latent Dirichlet allocation (LDA), we want the model to determine the number of topics needed. Consequently we follow the assumptions made by the hierarchical Dirichlet process (HDP) [1] of a countable but infinite number of topics, of which only a finite number is used in the posterior. Our prior is constructed by a truncated sick-breaking process [2],
Answer:
Practical Collapsed Stochastic Variational Inference for the HDP