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: Multi Sentence Compression (MSC) is of great value to many real world applications, such as guided microblog summarization, opinion summarization and newswire summarization. Recently, word graph-based approaches have been proposed and become popular in MSC. Their key assumption is that redundancy among a set of related sentences provides a reliable way to generate informative and grammatical sentences. In this paper, we propose an effective approach to enhance the word graph-based MSC and tackle the issue that most of the state-of-the-art MSC approaches are confronted with: i.e., improving both informativity and grammaticality at the same time. Our approach consists of three main components: (1) a merging method based on Multiword Expressions (MWE); (2) a mapping strategy based on synonymy between words; (3) a re-ranking step to identify the best compression candidates generated using a POS-based language model (POS-LM). We demonstrate the effectiveness of this novel approach using a dataset made of clusters of English newswire sentences. The observed improvements on informativity and grammaticality of the generated compressions show that our approach is superior to state-of-the-art MSC methods.
Example Output: On Improving Informativity and Grammaticality for Multi-Sentence Compression

Example Input: This article describes the systems jointly submitted by Institute for Infocomm (IR), the Laboratoire d’Informatique de l’Universit du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The submitted system is a fusion of nine sub-systems based on i-vectors [1] extracted from different types of features. Given the i-vectors, several classifiers are adopted for the language detection task including support vector machines (SVM) [2], multi-class logistic regression (MCLR), Probabilistic Linear Discriminant Analysis (PLDA) [3] and Deep Neural Networks (DNN).
Example Output: Fantastic 4 system for NIST 2015 Language Recognition Evaluation

Example Input: Planning for distributed agents with partial state information is considered from a decision­ theoretic perspective. We describe generaliza­ tions of both the MDP and POMDP models that allow for decentralized control. For even a small number of agents, the finite-horizon prob­ lems corresponding to both of our models are complete for nondeterministic exponential time. These complexity results illustrate a fundamen­ tal difference between centralized and decentral­ ized control of Markov processes. In contrast to the MDP and POMDP problems, the problems we consider provably do not admit polynomial­ time algorithms and most likely require doubly exponential time to solve in the worst case. We have thus provided mathematical evidence corre­ sponding to the intuition that decentralized plan­ ning problems cannot easily be reduced to cen­ tralized problems and solved exactly using estab­ lished techniques.
Example Output:
The Complexity of Decentralized Control of Markov Decision Processes