DebateSum: A large-scale argument mining and summarization dataset

14 Nov 2020  ·  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 read more

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Datasets


Introduced in the Paper:

DebateSum
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Extractive Text Summarization DebateSum Longformer-Base ROUGE-L 57.21 # 1
Extractive Text Summarization DebateSum BERT-Large ROUGE-L 49.98 # 3
Extractive Text Summarization DebateSum GPT2-Medium ROUGE-L 53.23 # 2

Methods