# DebateSum: A large-scale argument mining and summarization dataset

arXiv: https://arxiv.org/abs/2011.07251 · HTML: https://mlsanity.com/paper/2011.07251/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 30 of 34)

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- [OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset](https://mlsanity.com/paper/2406.14657/) (2024)
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- [MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections](https://mlsanity.com/paper/2502.00322/) (2025)
- [ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation](https://mlsanity.com/paper/2505.22076/) (2025)
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- [Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts](https://mlsanity.com/paper/2106.14438/) (2021)
- [Topic Ontologies for Arguments](https://mlsanity.com/paper/2301.09759/) (2023)
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- [Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs](https://mlsanity.com/paper/2407.04855/) (2024)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models](https://mlsanity.com/paper/2503.05328/) (2025)
- [ArgCMV: An Argument Summarization Benchmark for the LLM-era](https://mlsanity.com/paper/2508.19580/) (2025)
- [PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Arguments](https://mlsanity.com/paper/2602.08716/) (2026)
- [ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers](https://mlsanity.com/paper/2202.06690/) (2022)
- [PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization](https://mlsanity.com/paper/2412.12588/) (2024)
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- [A dataset of rated conceptual arguments](https://mlsanity.com/paper/2607.27499/) (2026)

## Abstract (arXiv)

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