Recent Trends in Unsupervised Summarization

Fuente: arXiv
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Autori principali: Khosravani, Mohammad, Trabelsi, Amine
Natura: Preprint
Pubblicazione: 2023
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author Khosravani, Mohammad
Trabelsi, Amine
author_facet Khosravani, Mohammad
Trabelsi, Amine
contents Unsupervised summarization is a powerful technique that enables training summarizing models without requiring labeled datasets. This survey covers different recent techniques and models used for unsupervised summarization. We cover extractive, abstractive, and hybrid models and strategies used to achieve unsupervised summarization. While the main focus of this survey is on recent research, we also cover some of the important previous research. We additionally introduce a taxonomy, classifying different research based on their approach to unsupervised training. Finally, we discuss the current approaches and mention some datasets and evaluation methods.
format Preprint
id arxiv_https___arxiv_org_abs_2305_11231
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Recent Trends in Unsupervised Summarization
Khosravani, Mohammad
Trabelsi, Amine
Computation and Language
Unsupervised summarization is a powerful technique that enables training summarizing models without requiring labeled datasets. This survey covers different recent techniques and models used for unsupervised summarization. We cover extractive, abstractive, and hybrid models and strategies used to achieve unsupervised summarization. While the main focus of this survey is on recent research, we also cover some of the important previous research. We additionally introduce a taxonomy, classifying different research based on their approach to unsupervised training. Finally, we discuss the current approaches and mention some datasets and evaluation methods.
title Recent Trends in Unsupervised Summarization
topic Computation and Language
url https://arxiv.org/abs/2305.11231