Abstract
Web page segmentation aims to break a large page into smaller blocks, in which contents with coherent semantics are kept together. Within this context, a great deal of approaches have been proposed without any specific end task in mind. In this paper, we study different segmentation strategies for the task of non visual skimming. For that purpose, we propose to segment web pages into visually coherent zones so that each zone can be represented by a set of relevant keywords that can be further synthesized into concurrent speech. As a consequence, we consider web page segmentation as a clustering problem of visual elements, where (1) a fixed number of clusters must be discovered, (2) the elements of a cluster should be visually connected and (3) all visual elements must be clustered. Therefore, we study variations of three existing algorithms, that comply to these constraints: K-means, F-K-means, and Guided Expansion. In particular, we evaluate different reading strategies for the positioning of the initial K seeds as well as a pre-clustering methodology for the Guided Expansion algorithm, which goal is to (1) fasten the clustering process and (2) reduce unbalance between clusters. The performed evaluation shows that the Guided Expansion algorithm evidences statistically increased results over the two other algorithms with the variations of the reading strategies. Nevertheless, improvements still need to be proposed to increase separateness.
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Notes
- 1.
- 2.
Oppositely to advertisement withdrawal for example.
- 3.
This is our unique use of the DOM structure.
- 4.
Illustration of this algorithm is presented in [2].
- 5.
Different strategies can be used to order the visual elements. In this paper, we use the order in which the visual elements appear in the DOM, using a depth-first search.
- 6.
All part of our project corpus.
- 7.
This situation is explained in detail in [2].
- 8.
For lack of space, we do not present this study in this paper.
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Andrew, J.J., Ferrari, S., Maurel, F., Dias, G., Giguet, E. (2020). Model-Driven Web Page Segmentation for Non Visual Access. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore. https://doi.org/10.1007/978-981-15-6168-9_17
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