Part 1. Definition
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.
Part 2. 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.
Answer: A Neural Local Coherence Model
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
Part 3. Exercise
This paper examines methods of decision making that are able to accommodate limitations on both the form in which uncertainty pertaining to a deci sion problem can be realistically represented and the amount of computing time available before a deci sion must be made. The methods are anytime algo "_th in the sense of Boddy and Dean [1989] . Tech Diques are presented for use with Frisch and Haddawy's [ 1992] anytime deduction system, with an anytime adaptation of Nilsson's [1986j probabilis tic logic, and with a probabilistic database model. 1 ANYTIME ALGORITHMS FOR
Answer:
Anytime Decision Making with Imprecise Probabilities