Web sites are designed for graphical mode of interaction. Sighted users can "cut to the chase" and quickly identify relevant information in Web pages. On the contrary, indi- viduals with visual disabilities have to use screen-readers to browse the Web. As screen-readers process pages sequen- tially and read through everything, Web browsing can be- come strenuous and time-consuming. Although, the use of shortcuts and searching offers some improvements, the prob- lem still remains. In this paper, we address the problem of information overload in non-visual Web access using the notion of context. Our prototype system, CSurf, embodying our approach, provides the usual features of a screen-reader. However, when a user follows a link, CSurf captures the context of the link using a simple topic-boundary detection technique, and uses it to identify relevant information on the next page with the help of a Support Vector Machine, a statistical machine-learning model. Then, CSurf reads the Web page starting from the most relevant section, identified by the model. We conducted a series experiments to eval- uate the performance of CSurf against the state-of-the-art screen-reader, JAWS. Our results show that the use of con- text can potentially save browsing time and substantially improve browsing experience of visually disabled people.
Slot:
New Brunswick, Wednesday, May 9, 2007, 3:30pm to 5:00pm.