@inproceedings{ravaut-etal-2022-towards,
title = "Towards Summary Candidates Fusion",
author = "Ravaut, Mathieu and
Joty, Shafiq and
Chen, Nancy",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.581",
doi = "10.18653/v1/2022.emnlp-main.581",
pages = "8488--8504",
abstract = "Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam. Recently, re-ranking methods have been proposed, to learn to select a better summary candidate. However, such methods are limited by the summary quality aspects captured by the first-stage candidates. To bypass this limitation, we propose a new paradigm in second-stage abstractive summarization called SummaFusion that fuses several summary candidates to produce a novel abstractive second-stage summary. Our method works well on several summarization datasets, improving both the ROUGE scores and qualitative properties of fused summaries. It is especially good when the candidates to fuse are worse, such as in the few-shot setup where we set a new state-of-the art. We will make our code and checkpoints available at \url{https://github.com/ntunlp/SummaFusion/}.",
}
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<abstract>Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam. Recently, re-ranking methods have been proposed, to learn to select a better summary candidate. However, such methods are limited by the summary quality aspects captured by the first-stage candidates. To bypass this limitation, we propose a new paradigm in second-stage abstractive summarization called SummaFusion that fuses several summary candidates to produce a novel abstractive second-stage summary. Our method works well on several summarization datasets, improving both the ROUGE scores and qualitative properties of fused summaries. It is especially good when the candidates to fuse are worse, such as in the few-shot setup where we set a new state-of-the art. We will make our code and checkpoints available at https://github.com/ntunlp/SummaFusion/.</abstract>
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%0 Conference Proceedings
%T Towards Summary Candidates Fusion
%A Ravaut, Mathieu
%A Joty, Shafiq
%A Chen, Nancy
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ravaut-etal-2022-towards
%X Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam. Recently, re-ranking methods have been proposed, to learn to select a better summary candidate. However, such methods are limited by the summary quality aspects captured by the first-stage candidates. To bypass this limitation, we propose a new paradigm in second-stage abstractive summarization called SummaFusion that fuses several summary candidates to produce a novel abstractive second-stage summary. Our method works well on several summarization datasets, improving both the ROUGE scores and qualitative properties of fused summaries. It is especially good when the candidates to fuse are worse, such as in the few-shot setup where we set a new state-of-the art. We will make our code and checkpoints available at https://github.com/ntunlp/SummaFusion/.
%R 10.18653/v1/2022.emnlp-main.581
%U https://aclanthology.org/2022.emnlp-main.581
%U https://doi.org/10.18653/v1/2022.emnlp-main.581
%P 8488-8504
Markdown (Informal)
[Towards Summary Candidates Fusion](https://aclanthology.org/2022.emnlp-main.581) (Ravaut et al., EMNLP 2022)
ACL
- Mathieu Ravaut, Shafiq Joty, and Nancy Chen. 2022. Towards Summary Candidates Fusion. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8488–8504, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.