Datasets:

Hellisotherpeople
/

DebateSum

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- crowdsourced
	language:
	- en
	license:
	- mit
	multilinguality:
	- monolingual
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	task_categories:
	- question-answering
	- summarization
	- text-retrieval
	- text-generation
	task_ids:
	- abstractive-qa
	- document-retrieval
	- extractive-qa
	pretty_name: 'DebateSum: A large-scale argument mining and summarization dataset'
	language_bcp47:
	- en-US
	tags:
	- conditional-text-generation
	---

	# DebateSum
	Corresponding code repo for the upcoming paper at ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset"

	Arxiv pre-print available here: https://arxiv.org/abs/2011.07251

	Check out the presentation date and time here: https://argmining2020.i3s.unice.fr/node/9

	Full paper as presented by the ACL is here: https://www.aclweb.org/anthology/2020.argmining-1.1/

	Video of presentation at COLING 2020: https://underline.io/lecture/6461-debatesum-a-large-scale-argument-mining-and-summarization-dataset

	The dataset is distributed as csv files.

	A search engine over DebateSum (as well as some additional evidence not included in DebateSum) is available as [debate.cards](http://debate.cards/). It's very good quality and allows for the evidence to be viewed in the format that debaters use.

	# Data

	DebateSum consists of 187328 debate documents, arguements (also can be thought of as abstractive summaries, or queries), word-level extractive summaries, citations, and associated metadata organized by topic-year. This data is ready for analysis by NLP systems.

	## Download
	All data is accesable in a parsed format organized by topic year [here](https://mega.nz/folder/ZdQGmK6b#-0hoBWc5fLYuxQuH25feXg)

	Addtionally, the trained word-vectors for [debate2vec](https://github.com/Hellisotherpeople/debate2vec) are also found in that folder.

	## Regenerating it yourself

	This is useful as the debaters who produce the evidence release their work every year. Soon enough I will update to include the 2020-2021 topic.

	Step 1: Download all open evidence files from [Open Evidence](https://openev.debatecoaches.org/) and unzip them into a directory. The links are as follows:

	* [2019](https://s3.amazonaws.com/openev/2019OpenEv.zip) - Resolved: The United States federal government should substantially reduce Direct Commercial Sales and/or Foreign Military Sales of arms from the United States.
	* [2018](https://s3.amazonaws.com/openev/2018OpenEv.zip) - Resolved: The United States federal government should substantially reduce its restrictions on legal immigration to the United States.
	* [2017](https://s3.amazonaws.com/openev/2017OpenEv.zip) - Resolved: The United States federal government should substantially increase its funding and/or regulation of elementary and/or secondary education in the United States.
	* [2016](https://s3.amazonaws.com/openev/2016OpenEv.zip) - Resolved: The United States federal government should substantially increase its economic and/or diplomatic engagement with the People’s Republic of China.
	* [2015](https://s3.amazonaws.com/openev/2015OpenEv.zip) - Resolved: The United States federal government should substantially curtail its domestic surveil-lance.
	* [2014](https://s3.amazonaws.com/openev/2014OpenEv.zip) - Resolved: The United States federal government should substantially increase its non-military exploration and/or development of the Earth’s oceans.
	* [2013](https://s3.amazonaws.com/openev/2013OpenEv.zip) - Resolved: The United States federal government should substantially increase its economic en-gagement toward Cuba, Mexico or Venezuela.


	Step 2: Convert all evidence from docx files to html5 files using [pandoc](https://pandoc.org/) with this command:
	```
	for f in *.docx; do pandoc "$f" -s -o "${f%.docx}.html5"; done
	```

	Step 3: install the dependencies for make_debate_dataset.py.

	```
	pip install -r requirements.txt
	```

	Step 4: Modify the folder and file locations as needed for your system, and run make_debate_dataset.py

	```
	python3 make_debate_dataset.py
	```

	# Credits
	Huge thanks to [Arvind Balaji](https://github.com/arvind-balaji) for making debate.cards and being second author on this paper!