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        <title>Term-Driven Forward-Looking Claim Synthesis in Earnings Calls</title>
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        <namePart type="given">Chung-Chi</namePart>
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            <namePart type="given">Nicoletta</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Term-Driven Forward-Looking Claim Synthesis in Earnings Calls
%A Chen, Chung-Chi
%A Takamura, Hiroya
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F chen-takamura-2024-term
%X 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.
%U https://aclanthology.org/2024.lrec-main.1368/
%P 15752-15760

Markdown (Informal)

[Term-Driven Forward-Looking Claim Synthesis in Earnings Calls](https://aclanthology.org/2024.lrec-main.1368/) (Chen & Takamura, LREC-COLING 2024)

• Term-Driven Forward-Looking Claim Synthesis in Earnings Calls (Chen & Takamura, LREC-COLING 2024)

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.