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.

Let me give you an 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.
The answer to this example can be: A Neural Local Coherence Model
Here is why: 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

OK. solve this:
We discuss methodological issues related to the evaluation of unsupervised binary code construction methods for nearest neighbor search. These issues have been widely ignored in literature. These coding methods attempt to preserve either Euclidean distance or angular (cosine) distance in the binary embedding space. We explain why when comparing a method whose goal is preserving cosine similarity to one designed for preserving Euclidean distance, the original features should be normalized by mapping them to the unit hypersphere before learning the binary mapping functions. To compare a method whose goal is to preserves Euclidean distance to one that preserves cosine similarity, the original feature data must be mapped to a higher dimension by including a bias term in binary mapping functions. These conditions ensure the fair comparison between different binary code methods for the task of nearest neighbor search. Our experiments show under these conditions the very simple methods (e.g. LSH and ITQ) often outperform recent state-of-the-art methods (e.g. MDSH and OK-means).
Answer:
Comparing apples to apples in the evaluation of binary coding methods