Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Computational Models of Argument |
| Untertitel | Proceedings of COMMA 2022 |
| Herausgeber/-innen | Francesca Toni, Sylwia Polberg, Richard Booth, Martin Caminada, Hiroyuki Kido |
| Erscheinungsort | Amsterdam |
| Herausgeber (Verlag) | IOS Press |
| Seiten | 21-31 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 9781643683072 |
| ISBN (Print) | 9781643683065 |
| Publikationsstatus | Veröffentlicht - Sept. 2022 |
| Extern publiziert | Ja |
| Veranstaltung | 9th International Conference on Computational Models of Argument, COMMA 2022 - Wales, Großbritannien / Vereinigtes Königreich Dauer: 14 Sept. 2022 → 16 Sept. 2022 |
Publikationsreihe
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Band | 353 |
| ISSN (Print) | 0922-6389 |
| ISSN (elektronisch) | 1879-8314 |
Abstract
In argument search, snippets provide an overview of the aspects discussed by the arguments retrieved for a queried controversial topic. Existing work has focused on generating snippets that are representative of an argument's content while remaining argumentative. In this work, we argue that the snippets should also be contrastive, that is, they should highlight the aspects that make an argument unique in the context of others. Thereby, aspect diversity is increased and redundancy is reduced. We present and compare two snippet generation approaches that jointly optimize representativeness and contrastiveness. According to our experiments, both approaches have advantages, and one is able to generate representative yet sufficiently contrastive snippets.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
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- RIS
Computational Models of Argument: Proceedings of COMMA 2022. Hrsg. / Francesca Toni; Sylwia Polberg; Richard Booth; Martin Caminada; Hiroyuki Kido. Amsterdam: IOS Press, 2022. S. 21-31 (Frontiers in Artificial Intelligence and Applications; Band 353).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Generating Contrastive Snippets for Argument Search
AU - Alshomary, Milad
AU - Rieskamp, Jonas
AU - Wachsmuth, Henning
N1 - Funding Information: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 - 438445824. We would also like to thank the reviewers and the participants who took part in the manual evaluation of our methods.
PY - 2022/9
Y1 - 2022/9
N2 - In argument search, snippets provide an overview of the aspects discussed by the arguments retrieved for a queried controversial topic. Existing work has focused on generating snippets that are representative of an argument's content while remaining argumentative. In this work, we argue that the snippets should also be contrastive, that is, they should highlight the aspects that make an argument unique in the context of others. Thereby, aspect diversity is increased and redundancy is reduced. We present and compare two snippet generation approaches that jointly optimize representativeness and contrastiveness. According to our experiments, both approaches have advantages, and one is able to generate representative yet sufficiently contrastive snippets.
AB - In argument search, snippets provide an overview of the aspects discussed by the arguments retrieved for a queried controversial topic. Existing work has focused on generating snippets that are representative of an argument's content while remaining argumentative. In this work, we argue that the snippets should also be contrastive, that is, they should highlight the aspects that make an argument unique in the context of others. Thereby, aspect diversity is increased and redundancy is reduced. We present and compare two snippet generation approaches that jointly optimize representativeness and contrastiveness. According to our experiments, both approaches have advantages, and one is able to generate representative yet sufficiently contrastive snippets.
KW - argument presentation
KW - argument search
KW - snippet generation
UR - http://www.scopus.com/inward/record.url?scp=85139514425&partnerID=8YFLogxK
U2 - 10.3233/FAIA220138
DO - 10.3233/FAIA220138
M3 - Conference contribution
AN - SCOPUS:85139514425
SN - 9781643683065
T3 - Frontiers in Artificial Intelligence and Applications
SP - 21
EP - 31
BT - Computational Models of Argument
A2 - Toni, Francesca
A2 - Polberg, Sylwia
A2 - Booth, Richard
A2 - Caminada, Martin
A2 - Kido, Hiroyuki
PB - IOS Press
CY - Amsterdam
T2 - 9th International Conference on Computational Models of Argument, COMMA 2022
Y2 - 14 September 2022 through 16 September 2022
ER -