Sequential Document Visualization Based on Hierarchical Parametric Histogram Curves

Recently, sequential document visualization has attracted much attention for its superior capability in depicting the sequential semantic progression in a single document. However, existing methods commonly take abstractive visual forms such as texts, numbers, and glyphs, and require much user expertise for document exploration. In this paper we propose a sequential visualization to represent a single document with a two- dimensional picture-based storyline, which semantically enhances the comprehension of textual information. We introduce a new parametric modeling approach called the Hierarchical Parametric Histogram Curve （HPHC）, which encodes the statistical progression locally and adaptively. By transforming an HPHC into the two-dimensional space with a new locality-preserving embedding algorithm, we create a mapping from points along the curve to descriptive pictures and generate the visualization result. The new representation expresses the primary content with a graphical form, and allows for efficient multi-resolution and focus＋context exploration in a long document. Our approach compares favorably with previous work in that it is more intuitive and requires less user expertise. Informal evaluation shows that it is useful in quick document browsing, communication, and understanding, especially for people with low literacy skills.