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.
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Question: This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.

Answer: Generating Sequences With Recurrent Neural Networks


Question: The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.

Answer: POLYGLOT-NER: Massive Multilingual Named Entity Recognition


Question: 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.

Answer:
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networks