Summary of A Hybrid Intelligence Method For Argument Mining, by Michiel Van Der Meer et al.
A Hybrid Intelligence Method for Argument Mining
by Michiel van der Meer, Enrico Liscio, Catholijn M. Jonker, Aske Plaat, Piek Vossen, Pradeep K. Murukannaiah
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a hybrid method called HyEnA that combines human and AI capabilities to extract arguments from opinionated texts. The goal is to understand opinions quickly and accurately by extracting key arguments from large-scale survey tools’ noisy datasets. Automated methods require labeled datasets, which induce high annotation costs, and work well for known viewpoints but not novel ones. HyEnA addresses these limitations by leveraging human insight while maintaining speed and reducing manual effort. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HyEnA is a new method that helps us understand opinions better by extracting key arguments from noisy data. Right now, we can use computers to do this job, but they need lots of training data and are not very good at understanding new ideas. HyEnA combines computer power with human understanding to get the best results. |
