An Adoption of a Contradiction Detection Task to Assist the Summarization of Online Debates


Nattapong Sanchan, Kalina Bontcheva, and Ahmet Aker

    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 Sanchan et al. (2017) 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: online debate summarization, text summarization, contradiction detection, information extraction, sentence extraction

    References:

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    @INPROCEEDINGS{2020_Sanchan_Bontcheva_Aker,
  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.