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:
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.

Ex Output:
Determining Semantic Textual Similarity using Natural Deduction Proofs


Ex Input:
The genetic selection of keywords set, the text frequencies of which are considered as attributes in text classification analysis, has been analyzed. The genetic optimization was performed on a set of words, which is the fraction of the frequency dictionary with given frequency limits. The frequency dictionary was formed on the basis of analyzed text array of texts of English fiction. As the fitness function which is minimized by the genetic algorithm, the error of nearest k neighbors classifier was used. The obtained results show high precision and recall of texts classification by authorship categories on the basis of attributes of keywords set which were selected by the genetic algorithm from the frequency dictionary.

Ex Output:
Genetic Optimization of Keywords Subset in the Classification Analysis of Texts Authorship


Ex Input:
<lb>Many canonical machine learning problems boil down to a convex optimization problem with a finite<lb>sum structure. However, whereas much progress has been made in developing faster algorithms for<lb>this setting, the inherent limitations of these problems are not satisfactorily addressed by existing lower<lb>bounds. Indeed, current bounds focus on first-order optimization algorithms, and only apply in the often<lb>unrealistic regime where the number of iterations is less than O(d/n) (where d is the dimension and n<lb>is the number of samples). In this work, we extend the framework of Arjevani et al. [3, 5] to provide<lb>new lower bounds, which are dimension-free, and go beyond the assumptions of current bounds, thereby<lb>covering standard finite sum optimization methods, e.g., SAG, SAGA, SVRG, SDCA without duality, as<lb>well as stochastic coordinate-descent methods, such as SDCA and accelerated proximal SDCA.

Ex Output:

Dimension-Free Iteration Complexity of Finite Sum Optimization Problems