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Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. Unsupervised stance detection for arguments from consequences An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews.
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Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect.
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When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Quantitative argument summarization and beyond: Cross-domain key point analysis Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation. presidential debates and online commentary, we demonstrate the effectiveness and limitations of the computational models. By evaluating the models on a corpus of 2016 U.S. In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. However, most argument mining systems and computational linguistics research have paid little attention to implicitly asserted propositions in argumentation. These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly. Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives.