OrderSum: Semantic Sentence Ordering for Extractive Summarization

• URL: http://arxiv.org/abs/2502.16180v1
• Date: Sat, 22 Feb 2025 10:51:04 GMT
• Title: OrderSum: Semantic Sentence Ordering for Extractive Summarization
• Authors: Taewan Kwon, Sangyong Lee,
• Abstract summary: OrderSum semantically orders sentences within an extractive summary.<n> OrderSum achieves a ROUGE-L score of 30.52 on CNN/DailyMail, outperforming the previous state-of-the-art model by a large margin of 2.54.
• Score: 0.8287206589886881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There are two main approaches to recent extractive summarization: the sentence-level framework, which selects sentences to include in a summary individually, and the summary-level framework, which generates multiple candidate summaries and ranks them. Previous work in both frameworks has primarily focused on improving which sentences in a document should be included in the summary. However, the sentence order of extractive summaries, which is critical for the quality of a summary, remains underexplored. In this paper, we introduce OrderSum, a novel extractive summarization model that semantically orders sentences within an extractive summary. OrderSum proposes a new representation method to incorporate the sentence order into the embedding of the extractive summary, and an objective function to train the model to identify which extractive summary has a better sentence order in the semantic space. Extensive experimental results demonstrate that OrderSum obtains state-of-the-art performance in both sentence inclusion and sentence order for extractive summarization. In particular, OrderSum achieves a ROUGE-L score of 30.52 on CNN/DailyMail, outperforming the previous state-of-the-art model by a large margin of 2.54.

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