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:
A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space. We present an ensemble method that combines embeddings produced by GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013) with structured knowledge from the semantic networks ConceptNet (Speer and Havasi, 2012) and PPDB (Ganitkevitch et al., 2013), merging their information into a common representation with a large, multilingual vocabulary. The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations. Its score of = .596 on an evaluation of rare words (Luong et al., 2013) is 16% higher than the previous best known system.

Ex Output:
An Ensemble Method to Produce High-Quality Word Embeddings

Ex Input:
Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN framework initially designed for electron microscope image segmentation. We hope that by sharing our volumetric vasculature datasets, we will inspire other researchers to experiment with vasculature dataset and improve the used network architectures.

Ex Output:
Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

Ex Input:
As artificial agents proliferate, it is becoming increasingly important to ensure that their interactions with one another are well-behaved. In this paper, we formalize a common-sense notion of when algorithms are well-behaved: an algorithm is safe if it does no harm. Motivated by recent progress in deep learning, we focus on the specific case where agents update their actions according to gradient descent. The first result is that gradient descent converges to a Nash equilibrium in safe games. The paper provides sufficient conditions that guarantee safe interactions. The main contribution is to define strongly-typed agents and show they are guaranteed to interact safely. A series of examples show that strong-typing generalizes certain key features of convexity and is closely related to blind source separation. The analysis introduce a new perspective on classical multilinear games based on tensor decomposition.

Ex Output:
Strongly-Typed Agents are Guaranteed to Interact Safely