Summarizing Dialogic Arguments from Social Media

Misra, Amita; Oraby, Shereen; Tandon, Shubhangi; Sharath, T S; Anand, Pranav; Walker, Marilyn A.

doi:10.21437/semdial.2017-14

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…It should be emphasized that ranking only aims at the practical necessity to give priority to some of the arguments; the user should have access to all arguments, and no filtering or censorship should take place as part of the ranking process. Preliminary research in this area has focused on identifying similar arguments using clustering techniques (Misra et al, 2015;Boltuzic and Snajder, 2016) and on summarizing the key issues brought up in debates using standard text summarization techniques (Ranade et al, 2013), tools and techniques from lexical semantics (Saint-Dizier, 2018), or machine learning techniques and word embeddings (Misra et al, 2017).…”

Section: Presentation and Visualizationmentioning

confidence: 99%

Sketching the vision of the Web of Debates

Bikakis

Flouris

Patkos

et al. 2023

Front. Artif. Intell.

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The exchange of comments, opinions, and arguments in blogs, forums, social media, wikis, and review websites has transformed the Web into a modern agora, a virtual place where all types of debates take place. This wealth of information remains mostly unexploited: due to its textual form, such information is difficult to automatically process and analyse in order to validate, evaluate, compare, combine with other types of information and make it actionable. Recent research in Machine Learning, Natural Language Processing, and Computational Argumentation has provided some solutions, which still cannot fully capture important aspects of online debates, such as various forms of unsound reasoning, arguments that do not follow a standard structure, information that is not explicitly expressed, and non-logical argumentation methods. Tackling these challenges would give immense added-value, as it would allow searching for, navigating through and analyzing online opinions and arguments, obtaining a better picture of the various debates for a well-intentioned user. Ultimately, it may lead to increased participation of Web users in democratic, dialogical interchange of arguments, more informed decisions by professionals and decision-makers, as well as to an easier identification of biased, misleading, or deceptive arguments. This paper presents the vision of the Web of Debates, a more human-centered version of the Web, which aims to unlock the potential of the abundance of argumentative information that currently exists online, offering its users a new generation of argument-based web services and tools that are tailored to their real needs.

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Section: Presentation and Visualizationmentioning

confidence: 99%

Sketching the vision of the Web of Debates

Bikakis

Flouris

Patkos

et al. 2023

Front. Artif. Intell.

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show abstract

“…Most of the computational argumentation methods, including those mentioned above, are supervised. Moreover, the studies focusing on argument identification (Swanson, Ecker, and Walker 2015;Misra et al 2017) , usually, rely on predefined lists of manually extracted arguments. As a first step towards unsupervised identification of prominent arguments from online debates, Boltužić and Šnajder (2015) group argumentative statements into clusters assimilated to arguments.…”

Section: Related Workmentioning

confidence: 99%

PhAITV: A Phrase Author Interaction Topic Viewpoint Model for the Summarization of Reasons Expressed by Polarized Stances

Trabelsi

Zaı̈ane

2019

ICWSM

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This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints in text documents. It proposes a pipeline framework that is centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint (PhAITV) model. The evaluation is conducted on all the components of the framework. It is mainly based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The framework shows a significant improvement over several configurations and state-of-the-art methods in contrastive summarization on online debate datasets.

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“…Most of the computational argumentation methods, are supervised. Even the studies focusing on argument identification [19,13] , usually, rely on predefined lists of manually extracted arguments. As a first step towards unsupervised identification of prominent arguments from online debates, Boltužić and Šnajder [4] group argumentative statements into clusters assimilated to arguments.…”

Section: Related Workmentioning

confidence: 99%

Contrastive Reasons Detection and Clustering from Online Polarized Debate

Trabelsi,

Zaiane

2019

Preprint

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This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues. It proposes a pipeline approach centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint model. The evaluation is based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The pipeline approach shows a significant improvement over state-of-the-art methods in contrastive summarization on online debate datasets.

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Summarizing Dialogic Arguments from Social Media

Cited by 9 publications

References 31 publications

Sketching the vision of the Web of Debates

Sketching the vision of the Web of Debates

PhAITV: A Phrase Author Interaction Topic Viewpoint Model for the Summarization of Reasons Expressed by Polarized Stances

Contrastive Reasons Detection and Clustering from Online Polarized Debate

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