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:
We introduce a new spatial data structure for high dimensional data called the approximate principal direction tree (APD tree) that adapts to the intrinsic dimension of the data. Our algorithm ensures vector-quantization accuracy similar to that of computationally-expensive PCA trees with similar time-complexity to that of loweraccuracy RP trees. APD trees use a small number of powermethod iterations to find splitting planes for recursively partitioning the data. As such they provide a natural trade-off between the running-time and accuracy achieved by RP and PCA trees. Our theoretical results establish a) strong performance guarantees regardless of the convergence rate of the powermethod and b) that O(log d) iterations suffice to establish the guarantee of PCA trees when the intrinsic dimension is d. We demonstrate this trade-off and the efficacy of our data structure on both the CPU and GPU.

Ex Output:
Approximate Principal Direction Trees


Ex Input:
A BN20 network is a two level belief net in which parent interactions are modeled using the noisy-or interaction model. In this paper we discuss application of the SPI local expression language [1] to effi­ cient inference in large BN20 networks. In particular, we show that there is sig­ nificant structure which can be exploited to improve over the Quickscore result. We further describe how symbolic tech­ niques can provide information which can significantly reduce the computation required for computing all cause poste­ rior marginals. Finally, we present a novel approximation technique with pre­ liminary experimental results.

Ex Output:
Symbolic Probabilistic Inference in large BN20 networks


Ex Input:
The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader’s reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.

Ex Output:

Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?