Topic development pattern analysis-based adaptation of information spaces

While navigation within complex information spaces is a challenge for all users, the problem is most evident with individuals who are blind or visually impaired. A particular challenge faced by students who are blind when accessing documents in digital libraries is that long documents are almost impenetrable for these users who cannot skim through large documents effectively and who cannot visually organize and re-organize documents for later use in new contexts. We highlight that adaptation and personalization of textual media can be possible only through novel algorithms that can segment media content to its basic information units and enable users to pick, recombine, and re-organize these units into new personalized documents. This is a multi-faceted problem that requires research into technical challenges from user modeling to context analysis. In this paper, we focus on two specific challenges key to the adaptation of textual media: content-segmentation and content-reorganization. In particular, we show that topic development analysis is fundamental in supporting both of these tasks. The algorithms proposed in this paper analyze topic development patterns without having to distill the specific topics, thereby keeping the overall analysis and adaptation processes light weight.