DeepAI
Log In Sign Up

DebateSum: A large-scale argument mining and summarization dataset

11/14/2020
by   Allen Roush, et al.
0

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

READ FULL TEXT
12/07/2020

CX DB8: A queryable extractive summarizer and semantic search engine

Competitive Debate's increasingly technical nature has left competitors ...
09/04/2022

ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

A challenging task when generating summaries of legal documents is the a...
10/29/2022

XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers

Transformers are among the state of the art for many tasks in speech, vi...
11/01/2020

Aspect-Based Argument Mining

Computational Argumentation in general and Argument Mining in particular...
10/31/2022

Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency

The topic of summarization evaluation has recently attracted a surge of ...
11/15/2020

Open4Business(O4B): An Open Access Dataset for Summarizing Business Documents

A major challenge in fine-tuning deep learning models for automatic summ...