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 propose a structured prediction architecture for images centered around deep recurrent neural networks. The proposed network, called ReSeg, is based on the recently introduced ReNet model for object classification. We modify and extend it to perform object segmentation, noting that the avoidance of pooling can greatly simplify pixel-wise tasks for images. The ReSeg layer is composed of four recurrent neural networks that sweep the image horizontally and vertically in both directions, along with a final layer that expands the prediction back to the original image size. ReSeg combines multiple ReSeg layers with several possible input layers as well as a final layer which expands the prediction back to the original image size, making it suitable for a variety of structured prediction tasks. We evaluate ReSeg on the specific task of object segmentation with three widely-used image segmentation datasets, namely Weizmann Horse, Fashionista and Oxford Flower. The results suggest that ReSeg can challenge the state of the art in object segmentation, and may have further applications in structured prediction at large.
Answer:
RESEG: A RECURRENT NEURAL NETWORK FOR OBJECT SEGMENTATION