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 Q]: This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis. The technique allows us to automatically extract low-dimensional features from high dimensional spectral features in a non-linear, data-driven, unsupervised way. We compared the new stochastic feature extractor with conventional mel-cepstral analysis in analysis-by-synthesis and text-to-speech experiments. Our results confirm that the proposed method increases the quality of synthetic speech in both experiments.
[EX A]: DEEP DENOISING AUTO-ENCODER FOR STATISTICAL SPEECH SYNTHESIS

[EX Q]: Automated Essay Scoring (AES) has been quite popular and is being widely used. However, lack of appropriate methodology for rating nonnative English speakers' essays has meant a lopsided advancement in this field. In this paper, we report initial results of our experiments with nonnative AES that learns from manual evaluation of nonnative essays. For this purpose, we conducted an exercise in which essays written by nonnative English speakers in test environment were rated both manually and by the automated system designed for the experiment. In the process, we experimented with a few features to learn about nuances linked to nonnative evaluation. The proposed methodology of automated essay evaluation has yielded a correlation coefficient of 0.750 with the manual evaluation.
[EX A]: Exploring Automated Essay Scoring for Nonnative English Speakers

[EX Q]: We treat collaborative filtering as a univari ate time series problem: given a user's previ ous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estima tion tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of collaborative filtering without ordering infor mation. The improvements in both predic tive accuracy and in recommendation quality that we realize advocate the use of predictive algorithms exploiting the temporal order of
[EX A]:
Using Temporal Data for Making Recommendations