RT info:eu-repo/semantics/article
T1 RankSum—An unsupervised extractive text summarization based on rank fusion
A1 Joshi, Akanksha
A1 Fidalgo Fernández, Eduardo
A1 Alegre Gutiérrez, Enrique
A1 Alaiz Rodríguez, Rocío
A2 Ingenieria de Sistemas y Automatica
K1 Documentación
K1 Lingüística
K1 Text summarization
K1 Extractive
K1 Topic
K1 Embeddings
K1 Keywords
K1 5701.02 Documentación Automatizada
K1 5701.05 Lenguajes Documentales
AB [EN] 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 generated in completely unsupervised way, and a labeled document set is required to learn the fusion weights. Since we found that the fusion weights can generalize to other datasets, we consider the Ranksum as an unsupervised approach. To determine topic rank, we employ probabilistic topic models whereas semantic information is captured using sentence embeddings. To derive rankings using sentence embeddings, we utilize Siamese networks to produce abstractive sentence representation and then we formulate a novel strategy to arrange them in their order of importance. A graph-based strategy is applied to find the significant keywords and related sentence rankings in the document. We also formulate a sentence novelty measure based on bigrams, trigrams, and sentence embeddings to eliminate redundant sentences from the summary. The ranks of all the sentences – computed for each feature – are finally fused to get the final score for each sentence in the document. We evaluate our approach on publicly available summarization datasets — CNN/DailyMail and DUC 2002. Experimental results show that our approach outperforms other existing state-of-the-art summarization methods.
PB Elsevier
SN 0957-4174
LK https://hdl.handle.net/10612/17676
UL https://hdl.handle.net/10612/17676
NO Joshi, A., Fidalgo, E., Alegre, E., & Alaiz-Rodriguez, R. (2022). RankSum—An unsupervised extractive text summarization based on rank fusion. Expert Systems with Applications, 200. https://doi.org/10.1016/J.ESWA.2022.116846
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 29-jun-2024