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:
One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.1

Ex Output:
Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation


Ex Input:
In this paper we present REG, a graph-based approach for study a fundamental problem of Natural Language Processing (NLP): the automatic text summarization. The algorithm maps a document as a graph, then it computes the weight of their sentences. We have applied this approach to summarize documents in three languages.

Ex Output:
Un résumeur à base de graphes, indépéndant de la langue


Ex Input:
We consider the problem of finding good finite-horizon policies for POMDPs under the expected reward metric. The policies con­ sidered are free finite-memory policies with limited memory; a policy is a mapping from the space of observation-memory pairs to the space of action-memory pairs (the policy up­ dates the memory as it goes), and the num­ ber of possible memory states is a parameter of the input to the policy-finding algorithms. The algorithms considered here are prelimi­ nary implementations of three search heuris­ tics: local search, simulated annealing, and genetic algorithms. We compare their out­ comes to each other and to the optimal poli­ cies for each instance. We compare run times of each policy and of a dynamic programming algorithm for POMDPs developed by Hansen that iteratively improves a finite-state con­ troller the previous state of the art for finite memory policies. The value of the best policy can only improve as the amount of memory increases, up to the amount needed for an optimal finite-memory policy. Our most surprising finding is that more memory helps in another way: given more memory than is needed for an optimal policy, the algo­ rithms are more likely to converge to optimal­ valued policies.

Ex Output:

My Brain is Full: When More Memory Helps