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:
In this paper we examine the benefit of performing named entity recognition (NER) and co-reference resolution to an English and a Greek corpus used for text segmentation. The aim here is to examine whether the combination of text segmentation and information extraction can be beneficial for the identification of the various topics that appear in a document. NER was performed manually in the English corpus and was compared with the output produced by publicly available annotation tools while, an already existing tool was used for the Greek corpus. Produced annotations from both corpora were manually corrected and enriched to cover four types of named entities. Co-reference resolution i.e., substitution of every reference of the same instance with the same named entity identifier was subsequently performed. The evaluation, using five text segmentation algorithms for the English corpus and four for the Greek corpus leads to the conclusion that, the benefit highly depends on the segment’s topic, the number of named entity instances appearing in it, as well as the segment’s length.

Ex Output:
Text Segmentation using Named Entity Recognition and Co-reference Resolution in English and Greek Texts


Ex Input:
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network architectures for whole slide image segmentation to accurately identify the tissue sections. We also compare the algorithms to a published traditional method. We collected 54 whole slide images with differing stains and tissue types from three laboratories to validate our algorithms. We show that while the two methods do not differ significantly they outperform their traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).

Ex Output:
COMPARISON OF DIFFERENT METHODS FOR TISSUE SEGMENTATION IN HISTOPATHOLOGICAL WHOLE-SLIDE IMAGES


Ex Input:
"Background subtraction" is an old technique for finding moving objects in a video sequence-for example, cars driving on a freeway. The idea is that subtracting the current image from a time­ averaged background image will leave only non­ stationary objects. It is, however, a crude ap­ proximation to the task of classifying each pixel of the current image; it fails with slow-moving objects and does not distinguish shadows from moving objects. The basic idea of this paper is that we can classify each pixel using a model of how that pixel looks when it is part of different classes. We learn a mixture-of-Gaussians classi­ fication model for each pixel using an unsuper­ vised technique-an efficient, incremental ver­ sion of EM. Unlike the standard image-averaging approach, this automatically updates the mixture component for each class according to likelihood of membership; hence slow-moving objects are handled perfectly. Our approach also identifies and eliminates shadows much more effectively than other techniques such as thresholding. Ap­ plication of this method as part of the Roadwatch traffic surveillance project is expected to result in significant improvements in vehicle identification and tracking.

Ex Output:

Image Segmentation in Video Sequences: A Probabilistic Approach