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 consider the problem of using sentence compression techniques to facilitate queryfocused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.
Example Output: A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

Example Input: We introduce an abductive method for a coherent integration of independent datasources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to ‘recover’ consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the ‘recovered databases’ in terms of the ‘preferred’ (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and – to the best of our knowledge – is more expressive (thus more general) than any other implementation of coherent integration of databases.
Example Output: Coherent Integration of Databases by Abductive Logic Programming

Example Input: Suppose we are given the conditional proba­ bility of one variable given some other vari­ ables. Normally the full joint distribution over the conditioning variables is required to determine the probability of the conditioned variable. Under what circumstances are the marginal distributions over the conditioning variables sufficient to determine the probabil­ ity of the conditioned variable? Sufficiency in this sense is equivalent to additive separa­ bility of the conditional probability distribu­ tion. Such separability structure is natural and can be exploited for efficient inference. Separability has a natural generalization to conditional separability. Separability provides a precise notion of hi­ erarchical decomposition in temporal prob­ abilistic models. Given a system that is decomposed into separable subsystems, ex­ act marginal probabilities over subsystems at future points in time can be computed by propagating marginal subsystem probabil­ ities, rather than complete system joint prob­ abilities. Thus, separability can make exact prediction tractable. However, observations can break separability, so exact monitoring of dynamic systems remains hard.
Example Output:
Sufficiency, Separability and Temporal Probabilistic Models