document: A graph-theoretic approach to webpage segmentation We consider the problem of segmenting a webpage into visually and semantically cohesive pieces . Our approach is based on formulating an appropriate optimization problem on weighted graphs , where the weights capture if two nodes in the DOM tree should be placed together or apart in the segmentation ; we present a learning framework to learn these weights from manually labeled data in a principled manner . Our work is a significant departure from previous heuristic and rule-based solutions to the segmentation problem . The results of our empirical analysis bring out interesting aspects of our framework , including variants of the optimization problem and the role of learning .
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