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.
--------
Question: 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.

Answer: Measuring Offensive Speech in Online Political Discourse

Question: Potential games and decentralised partially observable MDPs (DecPOMDPs) are two commonly used models of multiagent interaction, for static optimisation and sequential decision making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player's observations of others' actions are perturbed by random noise, and use this algorithm to construct an online learning method for solving DecPOMDPs. Specifically, we prove that noise in observations prevents standard fictitious play from converging to Nash equilibrium in potential games, which also makes fictitious play impractical for solving DecPOMDPs. To combat this, we derive filtered fictitious play, and provide conditions under which it converges to a Nash equilibrium in potential games with noisy observations. We then use filtered fictitious play to construct a solver for DecPOMDPs, and demonstrate our new algorithm's performance in a box pushing problem. Our results show that we consistently outperform the stateoftheart Dec POMDP solver by an average of 100% across the range of noise in the observation function.

Answer: Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs

Question: Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to re ect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the e ectiveness of our model by signi cantly outperforming the state-of-the-art. We furthermore demonstrate how an e ective contextualization method results in improving citation-based summarization of the scienti c articles.

Answer:
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge