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.

[Q]: 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.
[A]: Deep Recurrent Generative Decoder for Abstractive Text Summarization∗


[Q]: Abstract: We often encounter situations in which an experimenter wants to find, by sequential experimentation, xmax = arg maxx f(x), where f(x) is a (possibly unknown) function of a well controllable variable x. Taking inspiration from physics and engineering, we have designed a new method to address this problem. In this paper, we first introduce the method in continuous time, and then present two algorithms for use in sequential experiments. Through a series of simulation studies, we show that the method is effective for finding maxima of unknown functions by experimentation, even when the maximum of the functions drifts or when the signal to noise ratio is low.
[A]: Thompson sampling with the online bootstrap


[Q]: Jake Ryland Williams, ∗ James P. Bagrow, † Andrew J. Reagan, ‡ Sharon E. Alajajian, § Christopher M. Danforth, ¶ and Peter Sheridan Dodds ∗∗ School of Information, University of California, Berkeley 102 South Hall #4600 Berkeley, CA 94720-4600. Department of Mathematics & Statistics, Vermont Complex Systems Center, Computational Story Lab, & the Vermont Advanced Computing Core, The University of Vermont, Burlington, VT 05401. (Dated: September 29, 2017)
[A]:
Selection models of language production support informed text partitioning: an intuitive and practical, bag-of-phrases framework for text analysis