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Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization. (arXiv:2209.00930v1 [cs.CL])
Sept. 5, 2022, 1:15 a.m. | Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, Jinyoung Yeo
cs.CL updates on arXiv.org arxiv.org
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, …
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