DebateSum: A large-scale argument mining and summarization dataset

Allen Roush, Arvind Balaji
11/14/2020
cs.CL

Official code: Hellisotherpeople/DebateSum (55 stars, last commit 2021-12-02). 2 more implementations below.

Abstract

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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Code Implementations(3)

Hellisotherpeople/DebateSumOfficial100%

Corresponding code repo for the paper at COLING 2020 - ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset"

555PythonOct 20, 20204 years ago
debatedebatersevidence
OpenDebate/debate-cardsOfficial100%
MIT

Search engine for finding and downloading debate evidence

395JavaScriptAug 11, 20186 months agoMIT
cx-debatedebatelincoln-douglaspolicy-debate
Hellisotherpeople/debate2vecOfficial100%
GPL-3.0

Word-vectors created from a large corpus of competative debate evidence

40PythonOct 15, 20194 years agoGPL-3.0
debatedebate2vecembeddingsfasttextnlp+2 more

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Cite this paper

@article{roush2020debatesum,
  title  = {DebateSum: A large-scale argument mining and summarization dataset},
  author = {Allen Roush and Arvind Balaji},
  year   = {2020},
  eprint = {2011.07251},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2011.07251}
}

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