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.

Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output. We propose a model with a hierarchical char2word encoder, that takes individual characters both as input and output. We first argue that this hierarchical representation of the character encoder reduces computational complexity, and show that it improves translation performance. Secondly, by qualitatively studying attention plots from the decoder we find that the model learns to compress common words into a single embedding whereas rare words, such as names and places, are represented character by character.
NEURAL MACHINE TRANSLATION WITH CHARACTERS AND HIERARCHICAL ENCODING

As the education fees are becoming more expensive, more students apply for scholarships. Consequently, hundreds and even thousands of applications need to be handled by the sponsor. To solve the problems, some alternatives based on several attributes (criteria) need to be selected. In order to make a decision on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS and Weighted Product (WP) methods is applied to select the candidates for academic and non-academic scholarships at Universitas Islam Negeri Sunan Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS and Weighted Product FMADM methods can be used to select the most suitable candidates to receive the scholarships since the preference values applied in this method can show applicants with the highest eligibility. Keyword: Fuzzy Multiple Attribute Decision Making, TOPSIS, Weighted Product, Scholarship
A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

Movie ratings play an important role both in determining the likelihood of a potential viewer to watch the movie and in reflecting the current viewer satisfaction with the movie. They are available in several sources like the television guide, best-selling reference books, newspaper columns, and television programs. Furthermore, movie ratings are crucial for recommendation engines that track the behavior of all users and utilize the information to suggest items they might like. Movie ratings in most cases, thus, provide information that might be more important than movie feature-based data. It is intuitively appealing that information about the viewing preferences in movie genres is sufficient for predicting a genre of an unlabeled movie. In order to predict movie genres, we treat ratings as a feature vector, apply the Bernoulli event model to estimate the likelihood of a movies given genre, and evaluate the posterior probability of the genre of a given movie using the Bayes rule. The goal of the proposed technique is to efficiently use the movie ratings for the task of predicting movie genres. In our approach we attempted to answer the question: "Given the set of users who watched a movie, is it possible to predict the genre of a movie based on its ratings?" Our simulation results with MovieLens 100k data demonstrated the efficiency and accuracy of our proposed technique, achieving 59% prediction rate for exact prediction and 69% when including correlated genres.
A movie genre prediction based on Multivariate Bernoulli model and genre correlations