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 input: 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 output: 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
Q: In previous work [BGHK92, BGHK93], we have studied the random-worlds approacha particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowl edge base consisting of objective (first-order, slalisti cal, and defaull) infonnation. But allowing a know l edge base to contain only objective information is sometimes limiting. We occa<;ionally wish to include infonnation about degrees of belief in the knowledge base as well, because there are contexts in which old be1iefs represent important information that should influence new beliefs. In this paper, we describe three quite general techniques for extending a method that generates degrees of belief from objective informa tion to one that can make use of degrees of belief as well. All of our techniques are ha<;ed on well-known approaches, such a5 cross-emropy. We discuss gen eral connections between the techniques and in partic ular show that, although conceptually and technically quite different, all of the techniques give the same answer when applied to the random-worlds method.
A:
Generating New Beliefs From Old*