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.

Input: Consider Input: Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible.

Output: Unsupervised Diverse Colorization via Generative Adversarial Networks

Input: Consider Input: Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average.

Output: Representing Sentences as Low-Rank Subspaces

Input: Consider Input: Normalized web distance (NWD) is a similarity or normalized semantic distance based on the World Wide Web or any other large electronic database, for instance Wikipedia, and a search engine that returns reliable aggregate page counts. For sets of search terms the NWD gives a similarity on a scale from 0 (identical) to 1 (completely different). The NWD approximates the similarity according to all (upper semi)computable properties. We develop the theory and give applications. The derivation of the NWD method is based on Kolmogorov complexity.
Output: Web Similarity