Definition: 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: I. Abstract This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to improve predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models were constructed, with SVM giving the best results: an improvement of 12.9% over binary models was achieved for hold out cross validation by augmenting with pairwise correlation probabilities of the labels.

Exploring Correlation between Labels to improve Multi-Label Classification