DebateSum: A large-scale argument mining and summarization dataset

Allen Roush, Arvind Balaji


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 7year 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
Anthology ID:
2020.argmining-1.1
Volume:
Proceedings of the 7th Workshop on Argument Mining
Month:
December
Year:
2020
Address:
Online
Editors:
Elena Cabrio, Serena Villata
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–7
Language:
URL:
https://aclanthology.org/2020.argmining-1.1
DOI:
Bibkey:
Cite (ACL):
Allen Roush and Arvind Balaji. 2020. DebateSum: A large-scale argument mining and summarization dataset. In Proceedings of the 7th Workshop on Argument Mining, pages 1–7, Online. Association for Computational Linguistics.
Cite (Informal):
DebateSum: A large-scale argument mining and summarization dataset (Roush & Balaji, ArgMining 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.argmining-1.1.pdf
Code
 Hellisotherpeople/DebateSum +  additional community code
Data
DebateSum