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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | BAM: Benchmarking Argument Mining on Scientific Documents |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | The AAAI-22 Workshop on Scientific Document Understanding at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) |
| Event Type | workshop |
| Event Location | online due to COVID-19 |
| Event Start Date | March 1 - 2022 |
| Event End Date | March 1 - 2022 |
| Publisher | CEUR Workshop Proceedings |
| Abstract Text | In this paper, we present BAM, a unified Benchmark for Argument Mining (AM). We propose a method to homogenize both the evaluation process and the data to provide a common view in order to ultimately produce comparable results. Built as a four stage and end-to-end pipeline, the benchmark allows for the direct inclusion of additional argument miners to be evaluated. First, our system pre-processes a ground truth set used both for training and testing. Then, the benchmark calculates a total of four measures to assess different aspects of the mining process. To showcase an initial implementation of our approach, we apply our procedure and evaluate a set of systems on a corpus of scientific publications. With the obtained comparable results we can homogeneously assess the current state of AM in this domain. |
| Other Identification Number | merlin-id:22327 |
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