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.

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoderdecoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.
Neural Question Generation from Text: A Preliminary Study

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both ‘Barack Obama’ and ‘US’ may serve as positive training instances for the relation born in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.
Knowledge Base Population using Semantic Label Propagation

We train a generative convolutional neural network<lb>which is able to generate images of objects given object<lb>type, viewpoint, and color. We train the network in a su-<lb>pervised manner on a dataset of rendered 3D chair mod-<lb>els. Our experiments show that the network does not merely<lb>learn all images by heart, but rather finds a meaningful<lb>representation of a 3D chair model allowing it to assess<lb>the similarity of different chairs, interpolate between given<lb>viewpoints to generate the missing ones, or invent new chair<lb>styles by interpolating between chairs from the training set.<lb>We show that the network can be used to find correspon-<lb>dences between different chairs from the dataset, outper-<lb>forming existing approaches on this task.

Learning to Generate Chairs with Convolutional Neural Networks