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 Q]: Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval. Lacking an underlying vector space, the data are given as pairwise similarities only. The few algorithms available for such data do not scale to larger datasets. Focusing on probabilistic batch classifiers, the Indefinite Kernel Fisher Discriminant (iKFD) and the Probabilistic Classification Vector Machine (PCVM) are both effective algorithms for this type of data but, with cubic complexity. Here we propose an extension of iKFD and PCVM such that linear runtime and memory complexity is achieved for low rank indefinite kernels. Employing the Nyström approximation for indefinite kernels, we also propose a new almost parameter free approach to identify the landmarks, restricted to a supervised learning problem. Evaluations at several larger similarity data from various domains show that the proposed methods provides similar generalization capabilities while being easier to parametrize and substantially faster for large scale data.
[EX A]: Probabilistic classifiers with low rank indefinite kernels

[EX Q]: 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.
[EX A]: Connecting the dots: Summarizing and Structuring Large Document Collections Using Concept Maps

[EX Q]: We apply the principle of maximum entropy to select a unique joint probability distribution from the set of all joint probability distributions speci­ fied by a credal network. In detail, we start by showing that the unique joint distribution of a Bayesian tree coincides with the maximum en­ tropy model of its conditional distributions. This result, however, does not hold anymore for gen­ eral Bayesian networks. We thus present a new kind of maximum entropy models, which are computed sequentially. We then show that for all general Bayesian networks, the sequential max­ imum entropy model coincides with the unique joint distribution. Moreover, we apply the new principle of sequential maximum entropy to in­ terval Bayesian networks and more generally to credal networks. We especially show that this ap­ plication is equivalent to a number of small local entropy maximizations.
[EX A]:
Credal Networks under Maximum Entropy