Browse State-of-the-Art › Abstractive Dialogue Summarization
Abstractive Dialogue Summarization
16 papers with code · 0 benchmarks · 6 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
6 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
16 shown of 16 papers with code (36 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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14 May 2018 4 repositories listedWe introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
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8 Aug 2022 2 repositories listedA 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…
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1 May 2022 2 repositories listedWe propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine…
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27 Nov 2019 2 repositories listedThis paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries.
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2 Sep 2022 1 repository listedIn this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
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26 May 2022 1 repository listedWe advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs.
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3 Mar 2022 1 repository listed Syntology ran 9 of 15 samples · 6 unverified · 15 pointer-only (licence)In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem.
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1 Nov 2021 1 repository listedAbstractive conversation summarization has received growing attention while most current state-of-the-art summarization models heavily rely on human-annotated summaries.
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10 Sep 2021 1 repository listedTo capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and…
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16 Jun 2021 1 repository listedSummarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions.
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28 May 2021 1 repository listedIn this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.
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14 May 2021 1 repository listedProposal of large-scale datasets has facilitated research on deep neural models for news summarization.
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20 Oct 2020 1 repository listedIn detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.
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4 Oct 2020 1 repository listedText summarization is one of the most challenging and interesting problems in NLP.
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5 Feb 2019 1 repository listedDialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data.
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15 Sep 2018 1 repository listedNeural abstractive summarization has been increasingly studied, where the prior work mainly focused on summarizing single-speaker documents (news, scientific publications, etc).
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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