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 present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by back-propagation through time. Our main results center on a large corpus of 1.5 million beer reviews from BeerAdvocate. In generative mode, our network produces reviews on command, tailored to a star rating or item category. The generative model can also run in reverse, performing classification with surprising accuracy. Performance of the reverse model provides a straightforward way to determine what the generative model knows without relying too heavily on subjective analysis. Given a review, the model can accurately determine the corresponding rating and infer the beer’s category (IPA, Stout, etc.). We exploit this capability, tracking perceived sentiment and class membership as each character in a review is processed. Quantitative and qualitative empirical evaluations demonstrate that the model captures meaning and learns nonlinear dynamics in text, such as the effect of negation on sentiment, despite possessing no a priori notion of words. Because the model operates at the character level, it handles misspellings, slang, and large vocabularies without any machinery explicitly dedicated to the purpose.
Example Output: CHARACTER-LEVEL GENERATIVE TEXT MODELS

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: Classical Decision Theory provides a norma­ tive framework for representing and reason­ ing about complex preferences. Straightfor­ ward application of this theory to automate decision making is difficult due to high elic­ itation cost. In response to this problem, researchers have recently developed a num­ ber of qualitative, logic-oriented approaches for representing and reasoning about pref­ erences. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to pref­ erence elicitation and decision making that is grounded in class ical multi-attribute util­ ity theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about class es of deci­ sion alternatives can be used to further con­ strain the utility function and thus identify sup-optimal alternatives. This work demon­ strates that quantitative and qualitative ap­ proaches can be synergistically integrated to provide effective and flexible decision sup­ port.
Example Output:
A Hybrid Approach to Reasoning with Partially Elicited Preference Models