Detailed Instructions: 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.
See one example below:
Problem: 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: 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

Problem: This article is an overview of the SP theory of intelligence, which aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived as a brain-like system that receives ‘New’ information and stores some or all of it in compressed form as ‘Old’ information; and it is realised in the form of a computer model, a first version of the SP machine. The matching and unification of patterns and the concept of multiple alignment are central ideas. Using heuristic techniques, the system builds multiple alignments that are ‘good’ in terms of information compression. For each multiple alignment, probabilities may be calculated for associated inferences. Unsupervised learning is done by deriving new structures from partial matches between patterns and via heuristic search for sets of structures that are ‘good’ in terms of information compression. These are normally ones that people judge to be ‘natural’, in accordance with the ‘DONSVIC’ principle—the discovery of natural structures via information compression. The SP theory provides an interpretation for concepts and phenomena in several other areas including ‘computing’, aspects of mathematics and logic, the representation of knowledge, natural language processing, pattern recognition, several kinds of reasoning, information storage and retrieval, planning and problem solving, information compression, neuroscience, and human perception and cognition. Examples include the parsing and production of language with discontinuous dependencies in syntax, pattern recognition at multiple levels of abstraction and ∗Now published as The SP theory of intelligence: an overview (J G Wolff, Information, 4 (3), 283-341, 2013, doi:10.3390/info4030283). †Dr Gerry Wolff, BA (Cantab), PhD (Wales), CEng, MBCS (CITP); CognitionResearch.org, Menai Bridge, UK; jgw@cognitionresearch.org; +44 (0) 1248 712962; +44 (0) 7746 290775; Skype: gerry.wolff; Web: www.cognitionresearch.org. 1 ar X iv :1 30 6. 38 88 v4 [ cs .A I] 7 J an 2 01 5 its integration with part-whole relations, nonmonotonic reasoning and reasoning with default values, reasoning in Bayesian networks including ‘explaining away’, causal diagnosis, and the solving of a geometric analogy problem.
Solution:
The SP theory of intelligence: an overview∗