DialogSum Challenge: Summarizing Real-Life Scenario Dialogues

Yulong Chen, Yang Liu, Yue Zhang


Abstract
We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community. Real-life scenario dialogue summarization has a wide potential application prospect in chat-bot and personal assistant. It contains unique challenges such as special discourse structure, coreference, pragmatics, and social common sense, which require specific representation learning technologies to deal with. We carefully annotate a large-scale dialogue summarization dataset based on multiple public dialogue corpus, opening the door to all kinds of summarization models.
Anthology ID:
2021.inlg-1.33
Volume:
Proceedings of the 14th International Conference on Natural Language Generation
Month:
August
Year:
2021
Address:
Aberdeen, Scotland, UK
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
308–313
Language:
URL:
https://aclanthology.org/2021.inlg-1.33
DOI:
Bibkey:
Cite (ACL):
Yulong Chen, Yang Liu, and Yue Zhang. 2021. DialogSum Challenge: Summarizing Real-Life Scenario Dialogues. In Proceedings of the 14th International Conference on Natural Language Generation, pages 308–313, Aberdeen, Scotland, UK. Association for Computational Linguistics.
Cite (Informal):
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues (Chen et al., INLG 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.inlg-1.33.pdf
Data
DREAMDailyDialogDialogSumMuTualSAMSum Corpus