An evaluation and enhancement of densitometric fragmentation for content slicing reuse

Content slicing addresses the need of adaptive systems to reuse open corpus material by converting it into re-composable information objects. However this conversion is highly dependent upon the ability to correctly fragment pages into structurally sound atomic pieces. A recently suggested approach to fragmentation, which relies on densitometric page representation, claims to achieve high accuracy and time performance. Although it has been well received within the research community, a full evaluation of this approach and identification of strengths and weaknesses across a range of characteristics hasn't been performed. This paper proposes an independent evaluation of the approach with respect to granularity control, accuracy, time performance, content diversity and linguistic dependency. Moreover, this paper also provides a significant contribution to address important weaknesses discovered during the analysis, in order to improve the suitability and impact of the original algorithm within the context of content slicing.