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: Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be in­ creased. The first is energy saving in the building. The second is customer value-how the people in the building experience the ef­ fects of the actions of the agents. We give evi­ dence for the truth of this hypothesis through experimental findings related to tools for arti­ ficial decision making. A number of assump­ tions related to agent control, through moni­ toring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one un­ certainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic deci­ sion situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.
Example Output: Artificial Decision Making Under Uncertainty in Intelligent Buildings

Example Input: <lb>The support vector machine (SVM) is a widely used method for classification. Although many<lb>efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-<lb>scale problems. A nice property of SVM is that the non-support vectors have no effect on the resulting<lb>classifier. Motivated by this observation, we present fast and efficient screening rules to discard non-<lb>support vectors by analyzing the dual problem of SVM via variational inequalities (DVI). As a result,<lb>the number of data instances to be entered into the optimization can be substantially reduced. Some<lb>appealing features of our screening method are: (1) DVI is safe in the sense that the vectors discarded<lb>by DVI are guaranteed to be non-support vectors; (2) the data set needs to be scanned only once to run<lb>the screening, whose computational cost is negligible compared to that of solving the SVM problem; (3)<lb>DVI is independent of the solvers and can be integrated with any existing efficient solvers. We also show<lb>that the DVI technique can be extended to detect non-support vectors in the least absolute deviations<lb>regression (LAD). To the best of our knowledge, there are currently no screening methods for LAD. We<lb>have evaluated DVI on both synthetic and real data sets. Experiments indicate that DVI significantly<lb>outperforms the existing state-of-the-art screening rules for SVM, and is very effective in discarding<lb>non-support vectors for LAD. The speedup gained by DVI rules can be up to two orders of magnitude.
Example Output:
Scaling SVM and Least Absolute Deviations via Exact Data Reduction