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.
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Question: Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multidocument summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.

Answer: Connecting the dots: Summarizing and Structuring Large Document Collections Using Concept Maps


Question: 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.

Answer: Defaults and lnfinitesimals Defeasible Inference by Nonarchimedean Entropy Maximization


Question: LetF be a set of boolean functions. We present an algorithm for learningF∨ := {∨f∈Sf | S ⊆ F} from membership queries. Our algorithm asks at most |F| ·OPT(F∨) membership queries where OPT(F∨) is the minimum worst case number of membership queries for learning F∨. When F is a set of halfspaces over a constant dimension space or a set of variable inequalities, our algorithm runs in polynomial time. The problem we address has practical importance in the field of program synthesis, where the goal is to synthesize a program that meets some requirements. Program synthesis has become popular especially in settings aiming to help end users. In such settings, the requirements are not provided upfront and the synthesizer can only learn them by posing membership queries to the end user. Our work enables such synthesizers to learn the exact requirements while bounding the number of membership queries.

Answer:
Learning Disjunctions of Predicates