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.

We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs. We obtain performance competitive with or better than state-of-the-art approaches.
Improving sentence compression by learning to predict gaze

We develop a new semantics for defeasible infer ence based on extended probability measures al lowed to take infinitesimal values, on the inter pretation of defaults as generalized conditional probability constraints and on a preferred-model implementation of entropy-maximization.
Defaults and lnfinitesimals Defeasible Inference by Nonarchimedean Entropy Maximization

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi cal notion of Value of Informationthe expected im provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer tainty about its current value estimates for states. In this paper we investigate ways to represent and rea son about this uncertainty in algorithms where the sys tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q values based on these. These distributions are used to compute a myopic approximation to the value of infor mation for each action and hence to select the action that best balances exploration and exploitation.
Model based Bayesian Exploration