@inproceedings{zhang-etal-2023-summit,
title = "{S}umm{I}t: Iterative Text Summarization via {C}hat{GPT}",
author = "Zhang, Haopeng and
Liu, Xiao and
Zhang, Jiawei",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.714",
doi = "10.18653/v1/2023.findings-emnlp.714",
pages = "10644--10657",
abstract = "Existing text summarization systems have made significant progress in recent years, but typically generate summaries in a single step. The one-shot summarization setting is sometimes inadequate, however, as the generated summary may contain hallucinations or overlook important details related to the reader{'}s interests. In this paper, we address this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, closely resembling the iterative process humans undertake when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We evaluate the performance of our framework on three benchmark summarization datasets through empirical and qualitative analyses. We also conduct a human evaluation to validate the effectiveness of the model{'}s refinements and find a potential issue of over-correction.",
}
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<abstract>Existing text summarization systems have made significant progress in recent years, but typically generate summaries in a single step. The one-shot summarization setting is sometimes inadequate, however, as the generated summary may contain hallucinations or overlook important details related to the reader’s interests. In this paper, we address this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, closely resembling the iterative process humans undertake when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We evaluate the performance of our framework on three benchmark summarization datasets through empirical and qualitative analyses. We also conduct a human evaluation to validate the effectiveness of the model’s refinements and find a potential issue of over-correction.</abstract>
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%0 Conference Proceedings
%T SummIt: Iterative Text Summarization via ChatGPT
%A Zhang, Haopeng
%A Liu, Xiao
%A Zhang, Jiawei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-summit
%X Existing text summarization systems have made significant progress in recent years, but typically generate summaries in a single step. The one-shot summarization setting is sometimes inadequate, however, as the generated summary may contain hallucinations or overlook important details related to the reader’s interests. In this paper, we address this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, closely resembling the iterative process humans undertake when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We evaluate the performance of our framework on three benchmark summarization datasets through empirical and qualitative analyses. We also conduct a human evaluation to validate the effectiveness of the model’s refinements and find a potential issue of over-correction.
%R 10.18653/v1/2023.findings-emnlp.714
%U https://aclanthology.org/2023.findings-emnlp.714
%U https://doi.org/10.18653/v1/2023.findings-emnlp.714
%P 10644-10657
Markdown (Informal)
[SummIt: Iterative Text Summarization via ChatGPT](https://aclanthology.org/2023.findings-emnlp.714) (Zhang et al., Findings 2023)
ACL
- Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023. SummIt: Iterative Text Summarization via ChatGPT. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10644–10657, Singapore. Association for Computational Linguistics.