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
The AGM theory of belief revision has be come an important paradigm for investigat ing rational belief changes. Unfortunately, researchers working in this paradigm have re stricted much of their attention to rather sim ple representations of belief states, namely logically closed sets of propositional sen tences. In our opinion, this has resulted in a too abstract categorisation of belief change operations: expansion, revision, or contrac tion. Occasionally, in the AGM paradigm, also probabilistic belief changes have been considered, and it is widely accepted that the probabilistic version of expansion is con ditioning. However, we argue that it may be more correct to view conditioning and expan sion as two essentially different kinds of belief change, and that what we call constraining is a better candidate for being considered prob abilistic expansion.
Answer:
Probabilistic Belief Change: Expansion, Conditioning and Constraining