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: We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e.g., sequences of a pre-specified length). The model can be thought of as a simple version of the actor-critic model that uses an interpolation of the actor (the MLE-based token generation policy) and the critic (a value function that estimates the future values of the desired property) for decision making. We demonstrate that the approach is able to incorporate a variety of properties that cannot be handled by standard neural sequence decoders, such as sequence length and backward probability (probability of sources given targets), in addition to yielding consistent improvements in abstractive summarization and machine translation when the property to be optimized is BLEU or ROUGE scores.

Answer: Learning to Decode for Future Success

Question: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.

Answer: Determining Semantic Textual Similarity using Natural Deduction Proofs

Question: This paper examines methods of decision making that are able to accommodate limitations on both the form in which uncertainty pertaining to a deci sion problem can be realistically represented and the amount of computing time available before a deci sion must be made. The methods are anytime algo "_th in the sense of Boddy and Dean [1989] . Tech Diques are presented for use with Frisch and Haddawy's [ 1992] anytime deduction system, with an anytime adaptation of Nilsson's [1986j probabilis tic logic, and with a probabilistic database model. 1 ANYTIME ALGORITHMS FOR

Answer:
Anytime Decision Making with Imprecise Probabilities