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Computer Science > Computation and Language

arXiv:2208.04163 (cs)
[Submitted on 8 Aug 2022 (v1), last revised 25 Apr 2023 (this version, v2)]

Title:Abstractive Meeting Summarization: A Survey

Authors:Virgile Rennard, Guokan Shang, Julie Hunter, Michalis Vazirgiannis
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Abstract:A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization, a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models and evaluation metrics that have been used to tackle the problems.
Comments: pre-MIT Press publication version for TACL journal
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2208.04163 [cs.CL]
  (or arXiv:2208.04163v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2208.04163

Submission history

From: Guokan Shang [view email]
[v1] Mon, 8 Aug 2022 14:04:38 UTC (6,893 KB)
[v2] Tue, 25 Apr 2023 10:49:51 UTC (6,683 KB)
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