Publications

@article{joshi_ranksumunsupervised_2022,

title = {RankSum—An unsupervised extractive text summarization based on rank fusion},

author = {Akanksha Joshi and Eduardo Fidalgo and Enrique Alegre and Rocio Alaiz-Rodriguez},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422002998},

year  = {2022},

date = {2022-01-01},

journal = {Expert Systems with Applications},

volume = {200},

pages = {116846},

abstract = {This paper introduces Ranksum, an approach for extractive text summarization that combines four multi-dimensional sentence features: topic information, semantic content, significant keywords, and position. It ranks sentences based on these features using an unsupervised method, followed by a weighted fusion to determine sentence significance. The method utilizes probabilistic topic models for topic ranking, sentence embeddings for semantic information, and graph-based strategies for identifying keywords. The approach also employs a novelty measure to avoid redundancy. Experimental results on datasets like CNN/DailyMail and DUC 2002 show that Ranksum outperforms existing summarization methods.},

note = {Publisher: Pergamon},

keywords = {Extractive Summarization, Sentence Ranking, Text summarization, Unsupervised Learning},

pubstate = {published},

tppubtype = {article}

}