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: In this paper, we briefly introduce MiniNLP, a natural language processing library for clinical narratives. MiniNLP is an experiment of our ideas on efficient and effective medical language processing. We introduce the overall design of MiniNLP and its major components, and show the performance of it in real projects.
Example Output: A Short Introduction to MiniNLP

Example Input: Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition. A potential problem of the PLDA model, however, is that it essentially assumes Gaussian distributions over speaker vectors, which is not always true in practice. Additionally, the objective function is not directly related to the goal of the task, e.g., discriminating true speakers and imposters. In this paper, we propose a max-margin metric learning approach to solve the problems. It learns a linear transform with a criterion that the margin between target and imposter trials are maximized. Experiments conducted on the SRE08 core test show that compared to PLDA, the new approach can obtain comparable or even better performance, though the scoring is simply a cosine computation.
Example Output: Max-Margin Metric Learning for Speaker Recognition

Example Input: State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant hand-tuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.
Example Output:
lpopt: A Rule Optimization Tool for Answer Set Programming Author=Manuel Bichler, Michael Morak, and Stefan Woltran