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.

Example Input: 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.
Example Output: Determining Semantic Textual Similarity using Natural Deduction Proofs

Example Input: In this paper we present architecture of a fuzzy expert system used for therapy of dyslalic children. With fuzzy approach we can create a better model for speech therapist decisions. A software interface was developed for validation of the system. The main objectives of this task are: personalized therapy (the therapy must be in according with child’s problems level, context and possibilities), speech therapist assistant (the expert system offer some suggestion regarding what exercises are better for a specific moment and from a specific child), (self) teaching (when system’s conclusion is different that speech therapist’s conclusion the last one must have the knowledge base change possibility).
Example Output: ARCHITECTURE OF A FUZZY EXPERT SYSTEM USED FOR DYSLALIC CHILDREN THERAPY

Example Input: Solving sequential decision making problems, such as text parsing, robotic<lb>control, and game playing, requires a combination of planning policies and gen-<lb>eralisation of those plans. In this paper, we present Expert Iteration, a novel al-<lb>gorithm which decomposes the problem into separate planning and generalisation<lb>tasks. Planning new policies is performed by tree search, while a deep neural net-<lb>work generalises those plans. In contrast, standard Deep Reinforcement Learning<lb>algorithms rely on a neural network not only to generalise plans, but to discover<lb>them too. We show that our method substantially outperforms Policy Gradients in<lb>the board game Hex, winning 84.4% of games against it when trained for equal<lb>time.
Example Output:
Thinking Fast and Slow with Deep Learning and Tree Search