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]: Several large cloze-style context-questionanswer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Our model outperforms models previously proposed for these tasks by a large margin.
[A]: Text Understanding with the Attention Sum Reader Network

[Q]: This paper describes the architecture and implementation of a rule-based grapheme to phoneme converter for Turkish. The system accepts surface form as input, outputs SAMPA mapping of the all parallel pronounciations according to the morphological analysis together with stress positions. The system has been implemented in Python.
[A]: Towards Turkish ASR: Anatomy of a rule-based Turkish g2p

[Q]: Dawid, Kjrerulff & Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact lo cal computations in junction trees. Utiliz ing the strengths of both methods, such hy brid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist ex act solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of ex act inference and Gibbs sampling in discrete Bayesian networks. We argue that this com bination calls for an extension of the usual message passing scheme of ordinary junction trees.
[A]:
HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees