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.

Ex Input:
Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an example, a dialog system might assemble a long and informative answer by sampling passages extracted from different documents retrieved from the web. We formulate the task of discourse connective prediction and release a dataset of 2.9M sentence pairs separated by discourse connectives for this task. Then, we evaluate the hardness of the task for human raters, apply a recently proposed decomposable attention (DA) model to this task and observe that the automatic predictor has a higher F1 than human raters (32 vs. 30). Nevertheless, under specific conditions the raters still outperform the DA model, suggesting that there is headroom for future improvements. Finally, we further demonstrate the usefulness of the connectives dataset by showing that it improves implicit discourse relation prediction when used for model pre-training.

Ex Output:
Automatic Prediction of Discourse Connectives


Ex Input:
Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

Ex Output:
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples


Ex Input:
There is a vast amount of unstructured Arabic information on the Web, this data is always organized in semi-structured text and cannot be used directly. This research proposes a semi-supervised technique that extracts binary relations between two Arabic named entities from the Web. Several works have been performed for relation extraction from Latin texts and as far as we know, there isn’t any work for Arabic text using a semi-supervised technique. The goal of this research is to extract a large list or table from named entities and relations in a specific domain. A small set of a handful of instance relations are required as input from the user. The system exploits summaries from Google search engine as a source text. These instances are used to extract patterns. The output is a set of new entities and their relations. The results from four experiments show that precision and recall varies according to relation type. Precision ranges from 0.61 to 0.75 while recall ranges from 0.71 to 0.83. The best result is obtained for (player, club) relationship, 0.72 and 0.83 for precision and recall respectively.

Ex Output:

EXTRACTING ARABIC RELATIONS FROM THE WEB