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.

Example input: 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.
Example output: A Neural Local Coherence Model
Example 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
Q: <lb>The support vector machine (SVM) is a widely used method for classification. Although many<lb>efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-<lb>scale problems. A nice property of SVM is that the non-support vectors have no effect on the resulting<lb>classifier. Motivated by this observation, we present fast and efficient screening rules to discard non-<lb>support vectors by analyzing the dual problem of SVM via variational inequalities (DVI). As a result,<lb>the number of data instances to be entered into the optimization can be substantially reduced. Some<lb>appealing features of our screening method are: (1) DVI is safe in the sense that the vectors discarded<lb>by DVI are guaranteed to be non-support vectors; (2) the data set needs to be scanned only once to run<lb>the screening, whose computational cost is negligible compared to that of solving the SVM problem; (3)<lb>DVI is independent of the solvers and can be integrated with any existing efficient solvers. We also show<lb>that the DVI technique can be extended to detect non-support vectors in the least absolute deviations<lb>regression (LAD). To the best of our knowledge, there are currently no screening methods for LAD. We<lb>have evaluated DVI on both synthetic and real data sets. Experiments indicate that DVI significantly<lb>outperforms the existing state-of-the-art screening rules for SVM, and is very effective in discarding<lb>non-support vectors for LAD. The speedup gained by DVI rules can be up to two orders of magnitude.
A:
Scaling SVM and Least Absolute Deviations via Exact Data Reduction