Abstract
Over the past two decades, several algorithms have been developed to segment a web page into semantically coherent units, a task with several applications in web content analysis. However, these algorithms have hardly been compared empirically and it thus remains unclear which of them—or rather, which of their underlying paradigms—performs best. To contribute to closing this gap, we report on the reproduction and comparative evaluation of five segmentation algorithms on a large, standardized benchmark dataset for web page segmentation: Three of the algorithms have been specifically developed for web pages and have been selected to represent paradigmatically different approaches to the task, whereas the other two approaches originate from the segmentation of photos and print documents, respectively. For a fair comparison, we tuned each algorithm’s parameters, if applicable, to the dataset. Altogether, the classic rule-based VIPS algorithm achieved the highest performance, closely followed by the purely visual approach of Cormier et al. For reproducibility, we provide our reimplementations of the algorithms along with detailed instructions.
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Notes
- 1.
Code + documentation: https://github.com/webis-de/ecir21-an-empirical-comparison-of-web-page-segmentation-algorithms
Provenance data: https://doi.org/10.5281/zenodo.4146889.
- 2.
Our port of https://github.com/tpopela/vips_java is available in the code repository of this paper.
- 3.
- 4.
- 5.
We use the model with X-101-64x4d-FPN backbone and c3-c5 DCN as available and suggested at https://github.com/open-mmlab/mmdetection/blob/master/configs/htc.
- 6.
The authors reported an erratum in their publication to us, so we used the corrected kernel size of \(3\times 3\) instead of \(5\times 5\) for layers conv6-1 and conv7-1.
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Acknowledgements
We thank the anonymous reviewers for their helpful comments and the authors of the respective algorithms for providing us with either their code and/or their support for our re-implementations, as stated in the respective section.
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Kiesel, J., Meyer, L., Kneist, F., Stein, B., Potthast, M. (2021). An Empirical Comparison of Web Page Segmentation Algorithms. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_5
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