Date
Publisher
arXiv
We introduce OpenDebateEvidence, a comprehensive dataset for argument mining
and summarization sourced from the American Competitive Debate community. This
dataset includes over 3.5 million documents with rich metadata, making it one
of the most extensive collections of debate evidence. OpenDebateEvidence
captures the complexity of arguments in high school and college debates,
providing valuable resources for training and evaluation. Our extensive
experiments demonstrate the efficacy of fine-tuning state-of-the-art large
language models for argumentative abstractive summarization across various
methods, models, and datasets. By providing this comprehensive resource, we aim
to advance computational argumentation and support practical applications for
debaters, educators, and researchers. OpenDebateEvidence is publicly available
to support further research and innovation in computational argumentation.
Access it here: https://huggingface.co/datasets/Yusuf5/OpenCaselist
Study at a glance
- Question: How well can AI models summarize complex arguments from competitive high school and college debates?
- Setting and data: Over 3.5 million documents from American competitive high school and college debates.
- Method: Dataset creation and experiments testing AI models on debate argument summarization.
Why it matters
Capturing the complexity of arguments in high school and college debates provides valuable resources for training and evaluation. This helps advance computational argumentation and support practical applications for debaters, educators, and researchers.
Key findings
- In American high school and college debates, advanced language models were effectively trained to summarize complex arguments.
- The language models effectively generated argumentative summaries across various testing methods, models, and datasets.
- Competitive debate evidence encompasses over 3.5 million documents that capture complex arguments across high school and college levels.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
