@inproceedings{louis-maynez-2023-opinesum,
title = "{O}pine{S}um: Entailment-based self-training for abstractive opinion summarization",
author = "Louis, Annie and
Maynez, Joshua",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.686",
doi = "10.18653/v1/2023.findings-acl.686",
pages = "10774--10790",
abstract = "A typical product or place often has hundreds of reviews, and summarization of these texts is an important and challenging problem. Recent progress on abstractive summarization in domains such as news has been driven by supervised systems trained on hundreds of thousands of news articles paired with human-written summaries. However for opinion texts, such large scale datasets are rarely available. Unsupervised methods, self-training, and few-shot learning approaches bridge that gap. In this work, we present a novel self-training approach, OpineSum for abstractive opinion summarization. The self-training summaries in this approach are built automatically using a novel application of textual entailment and capture the consensus of opinions across the various reviews for an item. This method can be used to obtain silver-standard summaries on a large scale and train both unsupervised and few-shot abstractive summarization systems. OpineSum outperforms strong peer systems in both settings.",
}
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%0 Conference Proceedings
%T OpineSum: Entailment-based self-training for abstractive opinion summarization
%A Louis, Annie
%A Maynez, Joshua
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F louis-maynez-2023-opinesum
%X A typical product or place often has hundreds of reviews, and summarization of these texts is an important and challenging problem. Recent progress on abstractive summarization in domains such as news has been driven by supervised systems trained on hundreds of thousands of news articles paired with human-written summaries. However for opinion texts, such large scale datasets are rarely available. Unsupervised methods, self-training, and few-shot learning approaches bridge that gap. In this work, we present a novel self-training approach, OpineSum for abstractive opinion summarization. The self-training summaries in this approach are built automatically using a novel application of textual entailment and capture the consensus of opinions across the various reviews for an item. This method can be used to obtain silver-standard summaries on a large scale and train both unsupervised and few-shot abstractive summarization systems. OpineSum outperforms strong peer systems in both settings.
%R 10.18653/v1/2023.findings-acl.686
%U https://aclanthology.org/2023.findings-acl.686
%U https://doi.org/10.18653/v1/2023.findings-acl.686
%P 10774-10790
Markdown (Informal)
[OpineSum: Entailment-based self-training for abstractive opinion summarization](https://aclanthology.org/2023.findings-acl.686) (Louis & Maynez, Findings 2023)
ACL