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: Building on recent advances in image caption generation and optical character recognition (OCR), we present a generalpurpose, deep learning-based system to decompile an image into presentational markup. While this task is a wellstudied problem in OCR, our method takes an inherently different, data-driven approach. Our model does not require any knowledge of the underlying markup language, and is simply trained end-to-end on real-world example data. The model employs a convolutional network for text and layout recognition in tandem with an attention-based neural machine translation system. To train and evaluate the model, we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup, as well as a synthetic dataset of web pages paired with HTML snippets. Experimental results show that the system is surprisingly effective at generating accurate markup for both datasets. While a standard domainspecific LaTeX OCR system achieves around 25% accuracy, our model reproduces the exact rendered image on 75% of examples.
Example Output: What You Get Is What You See: A Visual Markup Decompiler

Example Input: Recently, bidirectional recurrent network language models (biRNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful. However, bi-RNNLMs pose a number of challenges as they make use of the complete previous and future word context information. This impacts both training efficiency and their use within a lattice rescoring framework. In this paper these issues are addressed by proposing a novel neural network structure, succeeding word RNNLMs (suRNNLMs). Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words. This model can be trained much more efficiently than bi-RNNLMs and can also be used for lattice rescoring. Experimental results on a meeting transcription task (AMI) show the proposed model consistently outperformed uni-RNNLMs and yield only a slight degradation compared to bi-RNNLMs in N-best rescoring. Additionally, performance improvements can be obtained using lattice rescoring and subsequent confusion network decoding.
Example Output: FUTURE WORD CONTEXTS IN NEURAL NETWORK LANGUAGE MODELS

Example Input: Offline handwriting recognition systems require cropped text line images for both<lb>training and recognition. On the one hand, the annotation of position and tran-<lb>script at line level is costly to obtain. On the other hand, automatic line seg-<lb>mentation algorithms are prone to errors, compromising the subsequent recogni-<lb>tion. In this paper, we propose a modification of the popular and efficient multi-<lb>dimensional long short-term memory recurrent neural networks (MDLSTM-<lb>RNNs) to enable end-to-end processing of handwritten paragraphs. More partic-<lb>ularly, we replace the collapse layer transforming the two-dimensional represen-<lb>tation into a sequence of predictions by a recurrent version which can recognize<lb>one line at a time. In the proposed model, a neural network performs a kind of<lb>implicit line segmentation by computing attention weights on the image represen-<lb>tation. The experiments on paragraphs of Rimes and IAM database yield results<lb>that are competitive with those of networks trained at line level, and constitute a<lb>significant step towards end-to-end transcription of full documents.
Example Output:
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition