OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset

Allen G Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Ravid Shwartz-Ziv

Keywords: Debate, Argument Mining, Policy Debate, Debate Evidence, Dataset, Datasets, Summarization, Extractive Summarization

TL;DR: We introduce the largest and most expansive argument mining dataset ever gathered, totaling over 3.5 million competitive debate documents and their metadata.

Abstract:

We introduce OpenDebateEvidence, a comprehensive dataset for argument mining
and summarization sourced from the American Competitive Debate community.
This dataset includes over 3.5 million documents with rich metadata, making it
one of the most extensive collections of debate evidence. OpenDebateEvidence
captures the complexity of arguments in high school and college debates, pro-
viding valuable resources for training and evaluation. Our extensive experiments
demonstrate the efficacy of fine-tuning state-of-the-art large language models for
argumentative abstractive summarization across various methods, models, and
datasets. By providing this comprehensive resource, we aim to advance com-
putational argumentation and support practical applications for debaters, edu-
cators, and researchers. OpenDebateEvidence is publicly available to support
further research and innovation in computational argumentation. Access it here:
https://huggingface.co/datasets/Yusuf5/OpenCaselist.

Submission Number: 1576