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.

Input: Consider Input: 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

Output: A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection


Input: Consider Input: One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.1

Output: Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation


Input: Consider Input: Kimmo Kettunen, Eetu Mäkelä, Teemu Ruokolainen, Juha Kuokkala and Laura Löfberg 1 National Library of Finland, Centre for Preservation and Digitization, Mikkeli, Finland kimmo.kettunen@helsinki.fi 2 Aalto University, Semantic Computing Research Group, Espoo, Finland eetu.makela@aalto.fi 3 National Library of Finland, Centre for Preservation and Digitization, Mikkeli, Finland teemu.ruokolainen@helsinki.fi 4 University of Helsinki, Department of Modern Languages, Helsinki, Finland juha.kuokkala@helsinki.fi 5 Department of Linguistics and English Language, Lancaster University, UK l.lofberg@lancaster.ac.uk


Output: Old Content and Modern Tools – Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771–1910