Paragraph-by-paragraph machine translation seems to be for now, an unjustifiably complicated task for practical needs. It involves the complete understanding of the paragraph, the determination of discourse topic(s), goals, intentions, so that the output can be produced in accordance with the respective discourse rules and purposes. However, recognizing topic, goal, intention by a computer prog...

Reading this paper leads to some thoughts. Our main concerns are first that from paragraph “Symptoms” to paragraph “Doppler Resistive Index”, this paper looks like the paper of M. Belal and P. Abrams “Noninvasive methods of diagnosing bladder outlet obstruction in men. Part 1: Nonurodynamic approach” J Urol 2006; 176: 22-8 (not referenced by the authors) and second, more important, that the aut...

We propose and motivate a novel task: paragraph segmentation. We discuss and compare this task with text segmentation and discourse parsing. We present a system that performs the task with high accuracy. A variety of features is proposed and examined in detail. The best models turn out to include lexical, coherence, and structural features.

Over the years, the amount of information avmlable electromcally has grown mamfold There Is an increasing demand for automatic methods for text summarization Dommnindependent techniques for automauc summanzation by paragraph extractton have been proposed m (Salton et al , 1994, Salton et al , 1996b) In tins study, we attempt to evainate these methods by companng the automatlcally generated extr...

MOTIVATION The growing availability of full-text scientific articles raises the important issue of how to most efficiently digest full-text content. Although article titles and abstracts provide accurate and concise information on an article's contents, their brevity inevitably entails the loss of detail. Full-text articles provide those details, but require more time to read. The primary goal ...