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: Classical higher-order logic, when utilized as a meta-logic in which various other (classical and non-classical) logics can be shallowly embedded, is well suited for realising a universal logic reasoning approach. Universal logic reasoning in turn, as envisioned already by Leibniz, may support the rigorous formalisation and deep logical analysis of rational arguments within machines. A respective universal logic reasoning framework is described and a range of exemplary applications are discussed. In the future, universal logic reasoning in combination with appropriate, controlled forms of rational argumentation may serve as a communication layer between humans and intelligent machines. 1 Rational Argumentation Communication Interface between Humans and Machines The ambition to understand, model and implement rational argumentation and universal logical reasoning independent of the human brain has a long tradition in the history of humankind. It reaches back at least to the prominent study of syllogistic arguments by Aristoteles. Today, with the event of increasingly intelligent computer technology, the question is more topical than ever: if humans and intelligent machines are supposed to amicably coexists, interact and collaborate, appropriate forms of communication between them are required. For example, machines should be able to depict, assess and defend their (options for) actions and decisions in a form that is accessible to human understanding and judgement. This will be crucial for achieving a reconcilable and socially accepted integration of intelligent machines into everyday (human) life. The communication means between machines and humans should ideally be based on human-level, rational argumentation, which since ages forms the fundament of our social, juridical and scientific processes. Current developments in artificial intelligence, in contrast, put a strong focus on statistical information, machine learning and subsymbolic representations, all of which are rather detached from human-level rational explanation, understanding and judgement. The challenge thus is to complement and enhance these human-unfriendly forms of reasoning and knowledge representation in todays artificial intelligence systems with suitable explanations amenable to human cognition, that is, rational arguments. Via exchange of rational arguments at human-intuitive level the much needed mutual understanding and acceptance between humans and intelligent machines can eventually be guaranteed. This is particularly relevant for the assessment of machine actions in terms of legal, ethical, moral, social and cultural norms purported by humans. But what formalisms are available that could serve as a most general basis for the modeling of human-level rational arguments in machines?
A:
Universal Reasoning, Rational Argumentation and Human-Machine Interaction