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.

Example Input: Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi textindependent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.
Example Output: Deep Speaker Vectors for Semi Text-independent Speaker Verification

Example Input: We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM classifier to accomplish contour detection. The main challenge lies in adapting a pre-trained per-image CNN model for yielding per-pixel image features. We propose to base on the DenseNet architecture to achieve pixelwise fine-tuning and then consider a cost-sensitive strategy to further improve the learning with a small dataset of edge and non-edge image patches. In the experiment of contour detection, we look into the effectiveness of combining per-pixel features from different CNN layers and obtain comparable performances to the state-of-the-art on BSDS500.
Example Output: CONTOUR DETECTION USING COST-SENSITIVE CON-

Example Input: У статті заманіфестовано проект квантитативної параметризації усіх текстів І. Франка, що можливо реалізувати, створивши частотний словник усіх творів письменника і лише із застосуванням сучаних комп'ютерних розробок. Вказано сфери застосування, етапи, методику, принципи і специфіку укладання частотного словника мови другої половини ХІХ — поч. ХХ ст., якою писав І. Франко. Описано співвідношення частотного словника І. Франка із словником мови письменника та корпусом текстів.
Example Output:
Соломія Бук, Андрій Ровенчак