Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of The 8th Workshop on Argument Mining, |
| Pages | 184-189 |
| Number of pages | 6 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 8th Workshop on Argument Mining, ArgMining 2021 - Virtual, Punta Cana, Dominican Republic Duration: 10 Nov 2021 → 11 Nov 2021 |
Abstract
Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Software
- Social Sciences(all)
- Linguistics and Language
Cite this
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Proceedings of The 8th Workshop on Argument Mining,. 2021. p. 184-189.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
AU - Alshomary, Milad
AU - Gurke, Timon
AU - Syed, Shahbaz
AU - Heinisch, Philipp
AU - Spliethöver, Maximilian
AU - Cimiano, Philipp
AU - Potthast, Martin
AU - Wachsmuth, Henning
PY - 2021
Y1 - 2021
N2 - Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.
AB - Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.
UR - http://www.scopus.com/inward/record.url?scp=85118304667&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85118304667
SN - 9781954085923
SP - 184
EP - 189
BT - Proceedings of The 8th Workshop on Argument Mining,
T2 - 8th Workshop on Argument Mining, ArgMining 2021
Y2 - 10 November 2021 through 11 November 2021
ER -