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.

Ex Input:
Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake to our knowledge for the first time an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two largescale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus. All experiments are available at url_redacted1

Ex Output:
Data Selection Strategies for Multi-Domain Sentiment Analysis

Ex Input:
There is overwhelming evidence that human intelligence is a product of Darwinian evolution. Investigating the consequences of self-modification, and more precisely, the consequences of utility function self-modification, leads to the stronger claim that not only human, but any form of intelligence is ultimately only possible within evolutionary processes. Humandesigned artificial intelligences can only remain stable until they discover how to manipulate their own utility function. By definition, a human designer cannot prevent a superhuman intelligence from modifying itself, even if protection mechanisms against this action are put in place. Without evolutionary pressure, sufficiently advanced artificial intelligences become inert by simplifying their own utility function. Within evolutionary processes, the implicit utility function is always reducible to persistence, and the control of superhuman intelligences embedded in evolutionary processes is not possible. Mechanisms against utility function self-modification are ultimately futile. Instead, scientific effort toward the mitigation of existential risks from the development of superintelligences should be in two directions: understanding consciousness, and the complex dynamics of evolutionary systems.

Ex Output:
Non-Evolutionary Superintelligences Do Nothing, Eventually

Ex Input:
Recent years have seen the development of a number<lb>of methods for multiagent planning under uncertainty<lb>that scale to tens or even hundreds of agents. However,<lb>most of these methods either make restrictive assump-<lb>tions on the problem domain, or provide approximate<lb>solutions without any guarantees on quality. To allow<lb>for meaningful benchmarking through measurable qual-<lb>ity guarantees on a very general class of problems, this<lb>paper introduces a family of influence-optimistic upper<lb>bounds for factored Dec-POMDPs. Intuitively, we de-<lb>rive bounds on very large multiagent planning problems<lb>by subdividing them in sub-problems, and at each of<lb>these sub-problems making optimistic assumptions with<lb>respect to the influence that will be exerted by the rest<lb>of the system. We numerically compare the different<lb>upper bounds and demonstrate how, for the first time<lb>ever, we can achieve a non-trivial guarantee that the<lb>heuristic solution of problems with hundreds of agents<lb>is close to optimal. Furthermore, we provide evidence<lb>that the upper bounds may improve the effectiveness of<lb>heuristic influence search, and discuss further potential<lb>applications to multiagent planning.

Ex Output:
Influence-Optimistic Local Values for Multiagent Planning Extended Version