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 examine the benefit of performing named entity recognition (NER) and co-reference resolution to an English and a Greek corpus used for text segmentation. The aim here is to examine whether the combination of text segmentation and information extraction can be beneficial for the identification of the various topics that appear in a document. NER was performed manually in the English corpus and was compared with the output produced by publicly available annotation tools while, an already existing tool was used for the Greek corpus. Produced annotations from both corpora were manually corrected and enriched to cover four types of named entities. Co-reference resolution i.e., substitution of every reference of the same instance with the same named entity identifier was subsequently performed. The evaluation, using five text segmentation algorithms for the English corpus and four for the Greek corpus leads to the conclusion that, the benefit highly depends on the segment’s topic, the number of named entity instances appearing in it, as well as the segment’s length.
Example Output: Text Segmentation using Named Entity Recognition and Co-reference Resolution in English and Greek Texts

Example Input: Language learning is thought to be a highly complex process. One of the hurdles in learning a language is to learn the rules of syntax of the language. Rules of syntax are often ordered in that before one rule can applied one must apply another. It has been thought that to learn the order of n rules one must go through all n! permutations. Thus to learn the order of 27 rules would require 27! steps or 1.08889x10 steps. This number is much greater than the number of seconds since the beginning of the universe! In an insightful analysis the linguist Block ([Block 86], pp. 62-63, p.238) showed that with the assumption of transitivity this vast number of learning steps reduces to a mere 377 steps. We present a mathematical analysis of the complexity of Block’s algorithm. The algorithm has a complexity of order n given n rules. In addition, we improve Block’s results exponentially, by introducing an algorithm that has complexity of order less than n log n.
Example Output: On the complexity of learning a language: An improvement of Block’s algorithm

Example Input: A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious a large advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters δ and c in the classification function. That is to say, to optimize the SVM algorithm, the optimization of the two parameters play a huge role. Ant Colony Optimization (ACO) is optimization algorithm which simulate ants to find the optimal path. In the available literature, we mix the ACO algorithm and Parallel algorithm together to find a well parameters. Keyword: SVM, Parameters, ACO, OpenCL, Parallel I. SUPPORT VECTOR CLASSIFICATION AND PARAMETERS SVM is based on the principle of structural risk minimization,using limited training samples to obtain the higher generalization ability of decision function. Suppose a sample set (xi, yi) , where i = 1, 2...N means the number of training samples, x∈R means the sample characteristics, y ∈ {+1,−1} means the sample classification. SVM Classification function: y = ωx+ b (ω means weight vector,b means setover)
Example Output:
A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm