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    <abstract>Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent.</abstract>
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    <identifier type="doi">10.18653/v1/2022.wiesp-1.7</identifier>
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%0 Conference Proceedings
%T Full-Text Argumentation Mining on Scientific Publications
%A Binder, Arne
%A Hennig, Leonhard
%A Verma, Bhuvanesh
%Y Ghosal, Tirthankar
%Y Blanco-Cuaresma, Sergi
%Y Accomazzi, Alberto
%Y Patton, Robert M.
%Y Grezes, Felix
%Y Allen, Thomas
%S Proceedings of the first Workshop on Information Extraction from Scientific Publications
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online
%F binder-etal-2022-full
%X Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent.
%R 10.18653/v1/2022.wiesp-1.7
%U https://aclanthology.org/2022.wiesp-1.7/
%U https://doi.org/10.18653/v1/2022.wiesp-1.7
%P 54-66

Markdown (Informal)

[Full-Text Argumentation Mining on Scientific Publications](https://aclanthology.org/2022.wiesp-1.7/) (Binder et al., WIESP 2022)

• Full-Text Argumentation Mining on Scientific Publications (Binder et al., WIESP 2022)

ACL

• Arne Binder, Leonhard Hennig, and Bhuvanesh Verma. 2022. Full-Text Argumentation Mining on Scientific Publications. In Proceedings of the first Workshop on Information Extraction from Scientific Publications, pages 54–66, Online. Association for Computational Linguistics.