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 propose a novel fully-automated approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the is-a relation in the target language. Character-level classifiers are trained on the constructed datasets, and used in an optimal path discovery framework to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages.
Example Output: 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-level Classification

Example Input: Here, we propose a brain-inspired winner-take-all emotional neural network (WTAENN) and prove the universal approximation property for the novel architecture. WTAENN is a single layered feedforward neural network that benefits from the excitatory, inhibitory, and expandatory neural connections as well as the winner-take-all (WTA) competitions in the human brain’s nervous system. The WTA competition increases the information capacity of the model without adding hidden neurons. The universal approximation capability of the proposed architecture is illustrated on two example functions, trained by a genetic algorithm, and then applied to several competing recent and benchmark problems such as in curve fitting, pattern recognition, classification and prediction. In particular, it is tested on twelve UCI classification datasets, a facial recognition problem, three real world prediction problems (2 chaotic time series of geomagnetic activity indices and wind farm power generation data), two synthetic case studies with constant and nonconstant noise variance as well as k-selector and linear programming problems. Results indicate the general applicability and often superiority of the approach in terms of higher accuracy and lower model complexity, especially where low computational complexity is
Example Output: A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property

Example Input: We improve the computational complexity of online learning algorithms that require to often recompute least squares regression estimates of parameters. We propose two stochastic gradient descent schemes with randomisation in order to efficiently track the true solutions of the regression problems achieving an O(d) improvement in complexity, where d is the dimension of the data. The first algorithm assumes strong convexity in the regression problem, and we provide bounds on the error both in expectation and high probability (the latter is often needed to provide theoretical guarantees for higher level algorithms). The second algorithm deals with cases where strong convexity of the regression problem cannot be guaranteed and uses adaptive regularisation. We again give error bounds in both expectation and high probability. We apply our approaches to the linear bandit algorithms PEGE and ConfidenceBall and demonstrate significant gains in complexity in both cases. Since strong convexity is guaranteed by the PEGE algorithm, we lose only logarithmic factors in the regret performance of the algorithm. On the other hand, in the ConfidenceBall algorithm we adaptively regularise to ensure strong convexity, and this results in an Õ(n1/5)1 deterioration of the regret.
Example Output:
Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits