Abstract
Argument synthesis aims to generate rational claims, representing a fundamental objective in this field. While existing models excel in summarizing arguments and engaging in debates, we observe a critical gap in their ability to generate accurate arguments that incorporate forward-looking perspectives. In light of this observation, this paper introduces a novel task called “forward-looking claim planning.” We delve into this task by exploring the efficacy of well-performing classification and generation models. Furthermore, we propose several customized preprocessing methods that yield substantial performance improvements. Through comprehensive discussion and analysis, we also outline a future research agenda for the forward-looking claim planning task.- Anthology ID:
- 2024.lrec-main.1368
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 15752–15760
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1368
- DOI:
- Cite (ACL):
- Chung-Chi Chen and Hiroya Takamura. 2024. Term-Driven Forward-Looking Claim Synthesis in Earnings Calls. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15752–15760, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Term-Driven Forward-Looking Claim Synthesis in Earnings Calls (Chen & Takamura, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.1368.pdf