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.
Cite
CITATION STYLE
Chen, Y., Liu, Y., & Zhang, Y. (2021). DialogSum Challenge: Summarizing Real-Life Scenario Dialogues. In INLG 2021 - 14th International Conference on Natural Language Generation, Proceedings (pp. 308–313). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.inlg-1.33
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