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]: Speech analysis had been taken to a new level with the discovery of Reverse Speech (RS). RS is the discovery of hidden messages, referred as reversals, in normal speech. Works are in progress for exploiting the relevance of RS in different real world applications such as investigation, medical field etc. In this paper we represent an innovative method for preparing a reliable Software Requirement Specification (SRS) document with the help of reverse speech. As SRS act as the backbone for the successful completion of any project, a reliable method is needed to overcome the inconsistencies. Using RS such a reliable method for SRS documentation was developed. Keywords Reverse Speech, Software Requirement Specification (SRS), Speech Enhancement, Speech Recognition.
[EX A]: Software Requirement Specification Using Reverse Speech Technology

[EX Q]: We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.
[EX A]: Localized Multiple Kernel LearningA Convex Approach

[EX Q]: We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language processing. Current tests of machine comprehension pose questions whose answers can be inferred from some supporting text, and evaluate a model's response to the questions. The EpiReader is an end-to-end neural model comprising two components: the first component proposes a small set of candidate answers after comparing a question to its supporting text, and the second component formulates hypotheses using the proposed candidates and the question, then reranks the hypotheses based on their estimated concordance with the supporting text. We present experiments demonstrating that the EpiReader sets a new state-of-the-art on the CNN and Children's Book Test machine comprehension benchmarks, outperforming previous neural models by a significant margin.
[EX A]:
Natural Language Comprehension with the EpiReader