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.

[Q]: In this paper we present REG, a graph-based approach for study a fundamental problem of Natural Language Processing (NLP): the automatic text summarization. The algorithm maps a document as a graph, then it computes the weight of their sentences. We have applied this approach to summarize documents in three languages.
[A]: Un resumeur a base de graphes, independant de la langue

[Q]: The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD() to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradient TD (ATD) methods that (1) provide similar data efficiency benefits to least-squares methods, at a fraction of the computation and storage (2) significantly reduce parameter sensitivity compared to linear TD methods, and (3) are asymptotically unbiased. We illustrate these claims with a proof of convergence in expectation and experiments on several benchmark domains and a large-scale industrial energy allocation domain.
[A]: Accelerated Gradient Temporal Difference Learning

[Q]: 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' principlethe 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.
[A]:
The SP theory of intelligence: an overview