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.
One example is below.
Q: 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.
A: A Neural Local Coherence Model
Rationale: 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: This paper describes a new method for classifying a dataset that partitions elements into different categories. It has relations with neural networks but works in a different way, requiring only a single pass through the classifier to generate the weight sets. A grid structure is required and a novel idea of converting a row of real values into a 2-D or grid-like structure of value bands. Each cell in the band can then store a cell weight value and also a set of weights that represent its own importance to each of the output categories. For any input that needs to be categorised, all of the output weight lists for each relevant input cell can be retrieved and summed to produce a probability for what the correct output category is. So the relative importance of each input point to the output is distributed to each cell. The construction process itself can simply be the reinforcement of the weight values, without requiring an iterative adjustment process, making it potentially much faster.
A:
A Single-Pass Classifier for Categorical Data