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
We present two related methods for creating MasterPrints, synthetic fingerprints that a fingerprint verification system identifies as many different people. Both methods start with training a Generative Adversarial Network (GAN) on a set of real fingerprint images. The generator network is then used to search for images that can be recognized as multiple individuals. The first method uses evolutionary optimization in the space of latent variables, and the second uses gradient-based search. Our method is able to design a MasterPrint that a commercial fingerprint system matches to 22% of all users in a strict security setting, and 75% of all users at a looser security setting.
Answer:
DeepMasterPrint: Generating Fingerprints for Presentation Attacks