Debate Entailment Datasets

 

    About this dataset:

    Debate Entailment Dataset from the Term-based Clustering Approach (DEDT) and Debate Entailment Dataset from the X-means Clustering Approach (DEDX) were used in the paper An Adoption of a Contradiction Detection Task to Assist the Summarization of Online Debates. 

    XML Format:

    The datasets were stored in the XML file format as an example shown here.

    Description:

    The datatsets contains the following tag elements:
    1. pair id: the identification number which uniquely identifies each Hypothesis-Text pair.
    2. entailment: the class of the Hypothesis-Text pair which can be CONTRADICTION or NON-CONTRADICTION.
    3. bar: the label of a cluster (the bar in Chart Summary) where this Hypothesis-Text pair belongs to.
    4. recidpairs: the identification numbers of Text-Hypothesis pairs in the SSSD dataset.

    Dowload this dataset:

    If you use this dataset in your work, please cite our paper below.
    By downloading the datasets, I agree that the data will be used for the educational purposes only.

    [DOWNLOAD DEDX Dataset]

    [DOWNLOAD DEDT Dataset]

    References:

    Download this paper: [PDF]
    Dowload bibtex entry: [BibTex] 
    @INPROCEEDINGS{9310941,
  author={N. {Sanchan} and K. {Bontcheva} and A. {Aker}},
  booktitle={2020 - 5th International Conference on Information Technology (InCIT)}, 
  title={An Adoption of a Contradiction Detection Task to Assist the Summarization of Online Debates}, 
  year={2020},
  volume={},
  number={},
  pages={185-190},
  abstract={In online debates, there are two opposing sides in which proponents and opponents sentimentally make arguments on various controversial topics. Currently, most debate summarization systems have focused on the generation of generic summaries. However, we view that these summaries may not entirely fulfill the needs of readers. On some occasions, readers may need to access the actual arguments that the proponents and opponents are debating on. For these reasons, we aim to generate contradiction summaries from online debates. In this paper, we prepare new datasets grounded on the online debate summaries generated by [7] and investigate whether a contradiction detection task could be effectively used to assist the generation of contradictory summaries for online debates. We observe which combination of features provides success in classifying contradiction. Our observation into the features and the qualitative analysis highlight that the employed can detect contradiction in online debates. To improve the classification results, more insight on coreference techniques and world knowledge that is hidden in the text should be extensively focused.},
  keywords={classification;Internet;text analysis;contradiction detection task;proponents;opponents;debate summarization systems;contradiction summaries;online debate summaries;contradiction classification;qualitative analysis;coreference techniques;Task analysis;Global warming;Logistics;Feature extraction;Ocean temperature;Finance;Bars},
  doi={10.1109/InCIT50588.2020.9310941},
  ISSN={},
  month={Oct},}
    Rich text bibliography entry (for copy & paste into a word processor):
    N. Sanchan, K. Bontcheva and A. Aker, "An Adoption of a Contradiction Detection Task to Assist the Summarization of Online Debates," 2020 - 5th International Conference on Information Technology (InCIT), Chonburi, Thailand, 2020, pp. 185-190, doi: 10.1109/InCIT50588.2020.9310941.