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: 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 solution: 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

Problem: The sparse, hierarchical and modular processing of natural signals are characteristics that relate to the ability of humans to recognise objects with high accuracy. In this paper, we report a sparse feature processing and encoding method targeted at improving the recognition performance of automated object recognition system. Randomly distributed selection of localised gradient enhanced features followed by the application of aggregate functions represents a modular and hierarchical approach to detect the object features. These object features, in combination with minimum distance classifier, results in object recognition system accuracies of 93% using ALOI, 92% using COIL-100 databases and 69% using PASCAL visual object challenge 2007 database, respectively. Robustness of object recognition performance is tested for variations in noise, object scaling and object shifts. Finally, a comparison with 8 existing object recognition methods indicated an improvement in recognition accuracy of 10% in ALOI, 8% in case of COIL-100 databases and 10% in PASCAL visual object challenge 2007 database.
Solution: Sparse distributed localised gradient fused features of objects