RankSum An unsupervised extractive text summarization based on rank fusion

• Published in: arXiv.org
• Main Authors: Joshi, A, Fidalgo, E, Alegre, E, Alaiz-Rodriguez, R
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 07.02.2024
• Subjects: Artificial neural networks; Datasets; Documents; Semantics; Sentences

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 sent...