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Rethinking Domain Generalization Baselines
Metrics concerned:Accuracy
In this paper, we exploit the innate document segment structure for improving
the extractive summarization task. We build two text segmentation models and
find the most optimal strategy to introduce their output predictions in an
extractive summarization model. Experimental results on a corpus of scientific
articles show that extractive summarization benefits from using a highly
accurate segmentation method. In particular, most of the improvement is in
documents where the most relevant information is not at the beginning thus, we
conclude that segmentation helps in reducing the lead bias problem.
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