Summary of Fingen: a Dataset For Argument Generation in Finance, by Chung-chi Chen et al.
FinGen: A Dataset for Argument Generation in Finance
by Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers explore the possibility of generating arguments about future scenarios, specifically in the financial application domain. They propose three argument generation tasks and test them using representative generation models, finding that these tasks are still challenging for these models. The results highlight unresolved issues and challenges in this research direction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict what might happen in the future, like how much money someone will make or whether a company will succeed. Futurists do this all the time! In this paper, scientists are trying to help computers do something similar. They created three tasks where computers have to generate arguments about possible futures in finance. Unfortunately, these tasks were really hard for the computer models they used. This research shows how much work needs to be done to make computers better at predicting and arguing about the future. |
