Papers › Document Summarization with Text Segmentation

Document Summarization with Text Segmentation

20 Jan 2023arXiv:2301.08817archive 2025-07-28

Lesly Miculicich, Benjamin Han

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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Tasks

ArticlesDocument SummarizationExtractive SummarizationSegmentationText SegmentationText Summarization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text Summarization Arxiv HEP-TH citation graph ExtSum + oracle segmentation (extractive) ROUGE-1 49.49 #4 of 28 Archive leaderboard report
Text Summarization Arxiv HEP-TH citation graph ExtSum + oracle segmentation (extractive) ROUGE-2 21.04 #4 of 28 Archive leaderboard report
Text Summarization Arxiv HEP-TH citation graph ExtSum + oracle segmentation (extractive) ROUGE-L 44.34 #4 of 28 Archive leaderboard report
Text Summarization Arxiv HEP-TH citation graph ExtSum + supervised segmentation (extractive) ROUGE-1 49.11 #6 of 28 Archive leaderboard report
Text Summarization Arxiv HEP-TH citation graph ExtSum + supervised segmentation (extractive) ROUGE-2 20.68 #6 of 28 Archive leaderboard report
Text Summarization Arxiv HEP-TH citation graph ExtSum + supervised segmentation (extractive) ROUGE-L 44.01 #6 of 28 Archive leaderboard report

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