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 11–20 of 284 papers

TitleStatusHype
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple SentencesCode1
Identifying the Human Values behind ArgumentsCode1
M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and TranscriptsCode1
Mining Legal Arguments in Court DecisionsCode1
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument MiningCode1
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument MiningCode1
AMPERSAND: Argument Mining for PERSuAsive oNline DiscussionsCode1
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language ModelsCode1
Argumentative Link Prediction using Residual Networks and Multi-Objective LearningCode1
Aspect-Based Argument MiningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified