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.
One example: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Solution is here: A Neural Local Coherence Model
Explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage

Now, solve this: The explosive availability of remote sensing im-<lb>ages has challenged supervised classification algorithms such as<lb>Support Vector Machines (SVM), as training samples tend to<lb>be highly limited due to the expensive and laborious task of<lb>ground truthing. The temporal correlation and spectral similarity<lb>between multitemporal images have opened up an opportunity<lb>to alleviate this problem. In this study, a SVM-based Sequen-<lb>tial Classifier Training (SCT-SVM) approach is proposed for<lb>multitemporal remote sensing image classification. The approach<lb>leverages the classifiers of previous images to reduce the required<lb>number of training samples for the classifier training of an<lb>incoming image. For each incoming image, a rough classifier<lb>is firstly predicted based on the temporal trend of a set of<lb>previous classifiers. The predicted classifier is then fine-tuned<lb>into a more accurate position with current training samples. This<lb>approach can be applied progressively to sequential image data,<lb>with only a small number of training samples being required<lb>from each image. Experiments were conducted with Sentinel-2A<lb>multitemporal data over an agricultural area in Australia. Results<lb>showed that the proposed SCT-SVM achieved better classification<lb>accuracies compared with two state-of-the-art model transfer<lb>algorithms. Compared with those obtained without the assistance<lb>from previous images, the overall classification accuracy was<lb>improved from 76.2% to 93.8%, demonstrating that the lever-<lb>age of a priori information from previous images can provide<lb>advantageous assistance for later images in multitemporal image<lb>classification.
Solution:
Effective Sequential Classifier Training for Multitemporal Remote Sensing Image Classification