DebateSum: A large-scale argument mining and summarization dataset
Published in
Workshop on Argument Mining(2020)External Links:
TL
TL;DR
The DebateSum dataset, which consists of 187,386 unique pieces of evidence with corresponding argument and extractive summaries, is presented and a search engine for this dataset is presented, utilized extensively by members of the National Speech and Debate Association today.
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
References14 items
1
Extractive Snippet Generation for Arguments
2
From Arguments to Key Points: Towards Automatic Argument Summarization
3
Longformer: The Long-Document Transformer
4
Query Focused Multi-Document Summarization with Distant Supervision
5