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: Distributed controllers are oftentimes used in largescale SDN deployments where they run a myriad of network applications simultaneously. Such applications could have different consistency and availability preferences. These controllers need to communicate via east/west interfaces in order to synchronize their state information. The consistency and the availability of the distributed state information are governed by an underlying consistency model. Earlier, we suggested [1] the use of adaptivelyconsistent controllers that can autonomously tune their consistency parameters in order to meet the performance requirements of a certain application. In this paper, we examine the feasibility of employing adaptive controllers that are built on-top of tunable consistency models similar to that of Apache Cassandra. We present an adaptation strategy that uses clustering techniques (sequential k-means and incremental k-means) in order to map a given application performance indicator (χ) into a feasible consistency level (Φ) that can be used with the underlying tunable consistency model. In the cases that we modeled and tested, our results show that in the case of sequential k-means, with a reasonable number of clusters (≥ 50), a plausible mapping (low RMSE) could be estimated between the application performance indicators (χ) and the consistency level indicator (Φ). In the case of incremental k-means, the results also showed that a plausible mapping (low RMSE) could be estimated using a similar number of clusters (≥ 50) by using a small threshold (≃ 0.01).
Example Output: A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers

Example Input: Acoustic models using probabilistic linear discriminant analysis (PLDA) capture the correlations within feature vectors using subspaces which do not vastly expand the model. This allows high dimensional and correlated feature spaces to be used, without requiring the estimation of multiple high dimension covariance matrices. In this letter we extend the recently presented PLDA mixture model for speech recognition through a tied PLDA approach, which is better able to control the model size to avoid overfitting. We carried out experiments uisng the Switchboard corpus, with both mel frequency cepstral coefficient features and bottleneck feature derived from a deep neural network. Reductions in word error rate were obtained by using tied PLDA, compared with the PLDA mixture model, subspace Gaussian mixture models, and deep neural networks.
Example Output: Tied Probabilistic Linear Discriminant Analysis for Speech Recognition

Example Input: We present WIKIREADING, a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the structured knowledge base Wikidata by reading the text of the corresponding Wikipedia articles. The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs). We compare various state-of-the-art DNNbased architectures for document classification, information extraction, and question answering. We find that models supporting a rich answer space, such as word or character sequences, perform best. Our best-performing model, a word-level sequence to sequence model with a mechanism to copy out-of-vocabulary words, obtains an accuracy of 71.8%.
Example Output:
WIKIREADING: A Novel Large-scale Language Understanding Task over Wikipedia