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--- |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- crowdsourced |
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language: |
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- en |
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license: |
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- mit |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 100K<n<1M |
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source_datasets: |
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- original |
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task_categories: |
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- question-answering |
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- text-retrieval |
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- text-generation |
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task_ids: |
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- summarization |
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- document-retrieval |
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- extractive-qa |
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- abstractive-qa |
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pretty_name: 'DebateSum: A large-scale argument mining and summarization dataset' |
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language_bcp47: |
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- en-US |
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tags: |
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- conditional-text-generation |
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--- |
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# DebateSum |
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Corresponding code repo for the upcoming paper at ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset" |
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Arxiv pre-print available here: https://arxiv.org/abs/2011.07251 |
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Check out the presentation date and time here: https://argmining2020.i3s.unice.fr/node/9 |
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Full paper as presented by the ACL is here: https://www.aclweb.org/anthology/2020.argmining-1.1/ |
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Video of presentation at COLING 2020: https://underline.io/lecture/6461-debatesum-a-large-scale-argument-mining-and-summarization-dataset |
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The dataset is distributed as csv files. |
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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. |
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# Data |
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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. |
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## Download |
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All data is accesable in a parsed format organized by topic year [here](https://mega.nz/folder/ZdQGmK6b#-0hoBWc5fLYuxQuH25feXg) |
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Addtionally, the trained word-vectors for [debate2vec](https://github.com/Hellisotherpeople/debate2vec) are also found in that folder. |
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## Regenerating it yourself |
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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. |
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*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:* |
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* [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. |
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* [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. |
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* [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. |
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* [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. |
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* [2015](https://s3.amazonaws.com/openev/2015OpenEv.zip) - Resolved: The United States federal government should substantially curtail its domestic surveil-lance. |
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* [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. |
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* [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. |
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*Step 2: Convert all evidence from docx files to html5 files using [pandoc](https://pandoc.org/) with this command:* |
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``` |
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for f in *.docx; do pandoc "$f" -s -o "${f%.docx}.html5"; done |
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``` |
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*Step 3: install the dependencies for make_debate_dataset.py.* |
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``` |
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pip install -r requirements.txt |
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``` |
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*Step 4: Modify the folder and file locations as needed for your system, and run make_debate_dataset.py* |
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``` |
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python3 make_debate_dataset.py |
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``` |
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# Credits |
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Huge thanks to [Arvind Balaji](https://github.com/arvind-balaji) for making debate.cards and being second author on this paper! |
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