@inproceedings{khalifa-etal-2021-bag,
title = "A Bag of Tricks for Dialogue Summarization",
author = "Khalifa, Muhammad and
Ballesteros, Miguel and
McKeown, Kathleen",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.631",
doi = "10.18653/v1/2021.emnlp-main.631",
pages = "8014--8022",
abstract = "Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.",
}
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<abstract>Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.</abstract>
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%0 Conference Proceedings
%T A Bag of Tricks for Dialogue Summarization
%A Khalifa, Muhammad
%A Ballesteros, Miguel
%A McKeown, Kathleen
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F khalifa-etal-2021-bag
%X Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.
%R 10.18653/v1/2021.emnlp-main.631
%U https://aclanthology.org/2021.emnlp-main.631
%U https://doi.org/10.18653/v1/2021.emnlp-main.631
%P 8014-8022
Markdown (Informal)
[A Bag of Tricks for Dialogue Summarization](https://aclanthology.org/2021.emnlp-main.631) (Khalifa et al., EMNLP 2021)
ACL
- Muhammad Khalifa, Miguel Ballesteros, and Kathleen McKeown. 2021. A Bag of Tricks for Dialogue Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8014–8022, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.