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.

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 – a period covering several divisive political events including the 2016 US presidential elections.
Measuring Offensive Speech in Online Political Discourse

We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by back-propagation through time. Our main results center on a large corpus of 1.5 million beer reviews from BeerAdvocate. In generative mode, our network produces reviews on command, tailored to a star rating or item category. The generative model can also run in reverse, performing classification with surprising accuracy. Performance of the reverse model provides a straightforward way to determine what the generative model knows without relying too heavily on subjective analysis. Given a review, the model can accurately determine the corresponding rating and infer the beer’s category (IPA, Stout, etc.). We exploit this capability, tracking perceived sentiment and class membership as each character in a review is processed. Quantitative and qualitative empirical evaluations demonstrate that the model captures meaning and learns nonlinear dynamics in text, such as the effect of negation on sentiment, despite possessing no a priori notion of words. Because the model operates at the character level, it handles misspellings, slang, and large vocabularies without any machinery explicitly dedicated to the purpose.
CHARACTER-LEVEL GENERATIVE TEXT MODELS

<lb>We endow prioritised default logic (PDL) with argumentation semantics<lb>using the ASPIC framework for structured argumentation, and prove<lb>that the conclusions of the justified arguments are exactly the prioritised<lb>default extensions. Argumentation semantics for PDL will allow for the<lb>application of argument game proof theories to the process of inference<lb>in PDL, making the reasons for accepting a conclusion transparent and<lb>the inference process more intuitive. This also opens up the possibility for<lb>argumentation-based distributed reasoning and communication amongst<lb>agents with PDL representations of mental attitudes.

Argumentation Semantics for Prioritised Default Logic

