@inproceedings{30840,
  author       = {{Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}},
  pages        = {{8782 -- 8797}},
  title        = {{{The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments}}},
  year         = {{2022}},
}

@inproceedings{25297,
  author       = {{Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 8th Workshop on Argument Mining}},
  pages        = {{184 -- 189}},
  title        = {{{Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}}},
  year         = {{2021}},
}

@inproceedings{25295,
  author       = {{Gurcke, Timon and Alshomary, Milad and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 8th Workshop on Argument Mining}},
  pages        = {{67 -- 77}},
  title        = {{{Assessing the Sufficiency of Arguments through Conclusion Generation}}},
  year         = {{2021}},
}

@inproceedings{21178,
  abstract     = {{When engaging in argumentative discourse, skilled human debaters tailor
claims to the beliefs of the audience, to construct effective arguments.
Recently, the field of computational argumentation witnessed extensive effort
to address the automatic generation of arguments. However, existing approaches
do not perform any audience-specific adaptation. In this work, we aim to bridge
this gap by studying the task of belief-based claim generation: Given a
controversial topic and a set of beliefs, generate an argumentative claim
tailored to the beliefs. To tackle this task, we model the people's prior
beliefs through their stances on controversial topics and extend
state-of-the-art text generation models to generate claims conditioned on the
beliefs. Our automatic evaluation confirms the ability of our approach to adapt
claims to a set of given beliefs. In a manual study, we additionally evaluate
the generated claims in terms of informativeness and their likelihood to be
uttered by someone with a respective belief. Our results reveal the limitations
of modeling users' beliefs based on their stances, but demonstrate the
potential of encoding beliefs into argumentative texts, laying the ground for
future exploration of audience reach.}},
  author       = {{Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}},
  location     = {{Online}},
  pages        = {{224--233}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Belief-based Generation of Argumentative Claims}}},
  doi          = {{10.18653/v1/2021.eacl-main.17}},
  year         = {{2021}},
}