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: 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.
Solution is here: 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

Now, solve this: We develop a latent variable model and an efficient spectral algorithm motivated<lb>by the recent emergence of very large data sets of chromatin marks from multiple<lb>human cell types. A natural model for chromatin data in one cell type is a Hidden<lb>Markov Model (HMM); we model the relationship between multiple cell types by<lb>connecting their hidden states by a fixed tree of known structure.<lb>The main challenge with learning parameters of such models is that iterative meth-<lb>ods such as EM are very slow, while naive spectral methods result in time and<lb>space complexity exponential in the number of cell types. We exploit properties<lb>of the tree structure of the hidden states to provide spectral algorithms that are<lb>more computationally efficient for current biological datasets. We provide sample<lb>complexity bounds for our algorithm and evaluate it experimentally on biological<lb>data from nine human cell types. Finally, we show that beyond our specific model,<lb>some of our algorithmic ideas can be applied to other graphical models.
Solution:
Spectral Learning of Large Structured HMMs for Comparative Epigenomics