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]: Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarities as well as semantic and syntactic regularities. Instead, we aim at reviving interest in a model based on counts. We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurrence statistics of large text corpora. We show that this distance gives good performance on word similarity and analogy tasks, with a proper type and size of context, and a dimensionality reduction based on a stochastic low-rank approximation. Besides being both simple and intuitive, this method also provides an encoding function which can be used to infer unseen words or phrases. This becomes a clear advantage compared to predictive models which must train these new words.
[EX A]: Rehabilitation of Count-based Models for Word Vector Representations

[EX Q]: Binary embedding of high-dimensional data requires long codes to preserve the discriminative power of the input space. Traditional binary coding methods often suffer from very high computation and storage costs in such a scenario. To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix. The circulant structure enables the use of Fast Fourier Transformation to speed up the computation. Compared to methods that use unstructured matrices, the proposed method improves the time complexity from O(d2) to O(d log d), and the space complexity from O(d2) to O(d) where d is the input dimensionality. We also propose a novel time-frequency alternating optimization to learn data-dependent circulant projections, which alternatively minimizes the objective in original and Fourier domains. We show by extensive experiments that the proposed approach gives much better performance than the state-of-the-art approaches for fixed time, and provides much faster computation with no performance degradation for fixed number of bits.
[EX A]: Circulant Binary Embedding

[EX Q]: Although agreement between annotators who mark feature locations within images has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of foreground-background segmentation algorithms. Many researchers utilise ground truth in experimentation and more often than not this ground truth is derived from one annotator’s opinion. How does the difference in opinion affect an algorithm’s evaluation? A methodology is applied to four image processing problems to quantify the inter-annotator variance and to offer insight into the mechanisms behind agreement and the use of ground truth. It is found that when detecting linear structures annotator agreement is very low. The agreement in a structure’s position can be partially explained through basic image properties. Automatic segmentation algorithms are compared to annotator agreement and it is found that there is a clear relation between the two. Several ground truth estimation methods are used to infer a number of algorithm performances. It is found that: the rank of a detector is highly dependent upon the method used to form the ground truth; and that although STAPLE and LSML appear to represent the mean of the performance measured using individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade. Furthermore, one of the most commonly adopted combination methods—consensus voting— accentuates more obvious features, resulting in an overestimation of performance. It is concluded that in some datasets it is not possible to confidently infer an algorithm ranking when evaluating upon one ground truth.
[EX A]:
An Empirical Study into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation