Given the task definition, example input & output, solve the new input case.
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.
Output: A Neural Local Coherence Model
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

New input case for you: The efficiency of inference in both the Hugin and, most notably, the Shafer-Shenoy archi tectures can be improved by exploiting the independence relations induced by the incom ing messages of a clique. That is, the mes sage to be sent from a clique can be com puted via a factorization of the clique poten tial in the form of a junction tree. In this pa per we show that by exploiting such nested junction trees in the computation of messages both space and time costs of the conventional propagation methods may be reduced. The paper presents a structured way of exploit ing the nested junction trees technique to achieve such reductions. The usefulness of the method is emphasized through a thor ough empirical evaluation involving ten large real-world Bayesian networks and the Hugin inference algorithm.
Output:
Nested Junction Trees