Teacher: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.
Teacher: Now, understand the problem? Solve this instance: We present an end-to-end, multimodal, fully convolu-<lb>tional network for extracting semantic structures from doc-<lb>ument images. We consider document semantic structure<lb>extraction as a pixel-wise segmentation task, and propose a<lb>unified model that classifies pixels based not only on their<lb>visual appearance, as in the traditional page segmentation<lb>task, but also on the content of underlying text. Moreover,<lb>we propose an efficient synthetic document generation pro-<lb>cess that we use to generate pretraining data for our net-<lb>work. Once the network is trained on a large set of synthetic<lb>documents, we fine-tune the network on unlabeled real doc-<lb>uments using a semi-supervised approach. We systemati-<lb>cally study the optimum network architecture and show that<lb>both our multimodal approach and the synthetic data pre-<lb>training significantly boost the performance.
Student:
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks