OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset

TL;DR

OpenDebateEvidence is a large-scale dataset from the debate community designed to improve argument mining and summarization, enabling advanced research and practical applications in computational argumentation.

Contribution

The paper introduces a comprehensive, publicly available dataset with over 3.5 million debate documents, facilitating research in argument mining and summarization.

Findings

Fine-tuning large language models improves argumentative summarization.

The dataset enhances training and evaluation for argumentation tasks.

State-of-the-art models show promising results on the dataset.

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, providing 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 computational argumentation and support practical applications for debaters, educators, and researchers. OpenDebateEvidence is publicly available to support further research and innovation in computational argumentation. Access it here: https://huggingface.co/datasets/Yusuf5/OpenCaselist