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 parametric form of pooling, based on a Gaussian, which can be optimized alongside the features in a single global objective function. By contrast, existing pooling schemes are based on heuristics (e.g. local maximum) and have no clear link to the cost function of the model. Furthermore, the variables of the Gaussian explicitly store location information, distinct from the appearance captured by the features, thus providing a what/where decomposition of the input signal. Although the differentiable pooling scheme can be incorporated in a wide range of hierarchical models, we demonstrate it in the context of a Deconvolutional Network model (Zeiler et al. [22]). We also explore a number of secondary issues within this model and present detailed experiments on MNIST digits.

Ex Output:
Differentiable Pooling for Hierarchical Feature Learning


Ex Input:
We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoderdecoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-ofthe-art methods.

Ex Output:
Deep Recurrent Generative Decoder for Abstractive Text Summarization∗


Ex Input:
We study the problem of multiclass classification with an extremely large number<lb>of classes, with the goal of obtaining train and test time complexity logarithmic<lb>in the number of classes. We develop top-down tree construction approaches for<lb>constructing logarithmic depth trees. On the theoretical front, we formulate a new<lb>objective function, which is optimized at each node of the tree and creates dy-<lb>namic partitions of the data which are both pure (in terms of class labels) and<lb>balanced. We demonstrate that under favorable conditions, we can construct loga-<lb>rithmic depth trees that have leaves with low label entropy. However, the objective<lb>function at the nodes is challenging to optimize computationally. We address the<lb>empirical problem with a new online decision tree construction procedure. Exper-<lb>iments demonstrate that this online algorithm quickly achieves small error rates<lb>relative to more common O(k) approaches and simultaneously achieves signif-<lb>icant improvement in test error compared to other logarithmic training time ap-<lb>proaches.

Ex Output:

Logarithmic Time Online Multiclass prediction