SOTAVerified

Argument Mining

Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.

Source: AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Papers

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Showing 1–10 of 284 papers

TitleStatusHype
DebateSum: A large-scale argument mining and summarization datasetCode1
Detecting Arguments in CJEU Decisions on Fiscal State AidCode1
Argumentative Link Prediction using Residual Networks and Multi-Objective LearningCode1
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument MiningCode1
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple SentencesCode1
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language ModelsCode1
AMPERSAND: Argument Mining for PERSuAsive oNline DiscussionsCode1
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument MiningCode1
Argument Mining Driven Analysis of Peer-ReviewsCode1
Exploring the Potential of Large Language Models in Computational ArgumentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified