RankSum—An unsupervised extractive text summarization based on rank fusion
作者：
Highlights：
• A unified summarization framework with multi-dimensional sentence features.
• Novel method to rank sentences using probabilistic topic vectors.
• Sentences ranked with a new technique exploiting embeddings.
• Novelty parameter utilizing bigrams, trigrams and sentence embeddings.
• Rank-based fusion strategy for extractive summarization.
摘要
•A unified summarization framework with multi-dimensional sentence features.•Novel method to rank sentences using probabilistic topic vectors.•Sentences ranked with a new technique exploiting embeddings.•Novelty parameter utilizing bigrams, trigrams and sentence embeddings.•Rank-based fusion strategy for extractive summarization.
论文关键词：Text summarization,Extractive,Topic,Embeddings,Keywords
论文评审过程：Received 10 July 2021, Revised 3 March 2022, Accepted 6 March 2022, Available online 25 March 2022, Version of Record 7 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116846
