Papers › DebateSum: A large-scale argument mining and summarization dataset
DebateSum: A large-scale argument mining and summarization dataset
Allen Roush, Arvind Balaji
Prior work in Argument Mining frequently alludes to its potential applications in automatic debating systems. Despite this focus, almost no datasets or models exist which apply natural language processing techniques to problems found within competitive formal debate. To remedy this, we present the DebateSum dataset. DebateSum consists of 187,386 unique pieces of evidence with corresponding argument and extractive summaries. DebateSum was made using data compiled by competitors within the National Speech and Debate Association over a 7-year period. We train several transformer summarization models to benchmark summarization performance on DebateSum. We also introduce a set of fasttext word-vectors trained on DebateSum called debate2vec. Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today. The DebateSum search engine is available to the public here: http://www.debate.cards
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a130c85d44825679 · report
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Extractive Text Summarization | DebateSum | Longformer-Base | ROUGE-L | 57.21 | #1 of 3 | Archive leaderboard | report |
| Extractive Text Summarization | DebateSum | GPT2-Medium | ROUGE-L | 53.23 | #2 of 3 | Archive leaderboard | report |
| Extractive Text Summarization | DebateSum | BERT-Large | ROUGE-L | 49.98 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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