Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, Jinyoung Yeo
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Abstract
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences as additional context. Compared to previous work that solely relies on the input dialogue, SICK uses an external knowledge model to generate a rich set of commonsense inferences and selects the most probable one with a similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense as supervision, where the task of generating commonsense inferences is added upon summarizing the dialogue in a multi-task learning setting. Experimental results show that with injected commonsense knowledge, our framework generates more informative and consistent summaries than existing methods.- Anthology ID:
- 2022.coling-1.548
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6285–6300
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.548/
- DOI:
- Bibkey:
- Cite (ACL):
- Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, and Jinyoung Yeo. 2022. Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6285–6300, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (Kim et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.548.pdf
Export citation
@inproceedings{kim-etal-2022-mind,
title = "Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization",
author = "Kim, Seungone and
Joo, Se June and
Chae, Hyungjoo and
Kim, Chaehyeong and
Hwang, Seung-won and
Yeo, Jinyoung",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.548/",
pages = "6285--6300",
abstract = "In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences as additional context. Compared to previous work that solely relies on the input dialogue, SICK uses an external knowledge model to generate a rich set of commonsense inferences and selects the most probable one with a similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense as supervision, where the task of generating commonsense inferences is added upon summarizing the dialogue in a multi-task learning setting. Experimental results show that with injected commonsense knowledge, our framework generates more informative and consistent summaries than existing methods."
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%0 Conference Proceedings %T Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization %A Kim, Seungone %A Joo, Se June %A Chae, Hyungjoo %A Kim, Chaehyeong %A Hwang, Seung-won %A Yeo, Jinyoung %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F kim-etal-2022-mind %X In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that uses commonsense inferences as additional context. Compared to previous work that solely relies on the input dialogue, SICK uses an external knowledge model to generate a rich set of commonsense inferences and selects the most probable one with a similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense as supervision, where the task of generating commonsense inferences is added upon summarizing the dialogue in a multi-task learning setting. Experimental results show that with injected commonsense knowledge, our framework generates more informative and consistent summaries than existing methods. %U https://aclanthology.org/2022.coling-1.548/ %P 6285-6300
Markdown (Informal)
[Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization](https://aclanthology.org/2022.coling-1.548/) (Kim et al., COLING 2022)
- Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (Kim et al., COLING 2022)
ACL
- Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, and Jinyoung Yeo. 2022. Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6285–6300, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.