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RankSum An unsupervised extractive text summarization based on rank fusion
[article]
2024
arXiv
pre-print
In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position. The Ranksum obtains the sentence saliency rankings corresponding to each feature in an unsupervised way followed by the weighted fusion of the four scores to rank the sentences according to their significance. The scores are
arXiv:2402.05976v1
fatcat:s6tlmuqscjfabm5f6abnpbjgce