OrderSum: Semantic Sentence Ordering for Extractive Summarization

The sentence-level framework defines extractive summarization as an individual sentence selection problem, determining whether each sentence in a document should be included in the summary. However, the sentence-level framework often produces summaries that contain only general sentences or repeat important but similar sentences (Narayan et al., 2018b; Zhong et al., 2020). The summary-level framework overcomes this limitation by defining extractive summarization as a summary ranking problem rather than a sentence selection problem. The main idea of the summary-level framework is to generate a set of candidate summaries consisting of different sentences, and then rank them to select the best summary. By considering sentence composition at the entire summary level rather than sentence by sentence, this approach enables each sentence in the summary to convey different, specific information (Narayan et al., 2018b; Zhong et al., 2020). Previous work in both frameworks has primarily focused on improving which sentences to include in the summary, or in other words, sentence inclusion. However, to the best of our knowledge, the importance of sentence order in summaries has not been highlighted since the era of graph-based extractive summarization (Mihalcea and Ta-rau, 2004; Erkan and Radev, 2004). The sentence order of a text plays a crucial role not only in readability but also in its meaning (Yin et al., 2019; Lo-geswaran et al., 2018). Table 1 illustrates how the arXiv:2502.16180v1