Bridging Numerical Reasoning and Headline Generation for Enhanced Language Models

Vaishnavi R, Srimathi T, Aarthi S, Harini V

Abstract
Headline generation becomes a vital tool in the dynamic world of digital media, combining creativity and scientific rigor to engage readers while maintaining accuracy. However, accuracy is currently hampered by numerical integration problems, which affect both abstractive and extractive approaches. Sentences that are extracted from the original material are typically too short to accurately represent complex information. Our research introduces an innovative two-step training technique to tackle these problems, emphasizing the significance of enhanced numerical reasoning in headline development. Promising advances are presented by utilizing text-to-text processing capabilities of the T5 model and advanced NLP approaches like BERT and RoBERTa. With the help of external contributions and our dataset, our Flan-T5 model has been improved to demonstrate how these methods may be used to overcome numerical integration issues and improve the accuracy of headline production.
Anthology ID:
2024.semeval-1.117
Volume:
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
821–828
Language:
URL:
https://aclanthology.org/2024.semeval-1.117
DOI:
10.18653/v1/2024.semeval-1.117
Bibkey:
Cite (ACL):
Vaishnavi R, Srimathi T, Aarthi S, and Harini V. 2024. Bridging Numerical Reasoning and Headline Generation for Enhanced Language Models. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 821–828, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Bridging Numerical Reasoning and Headline Generation for Enhanced Language Models (R et al., SemEval 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.semeval-1.117.pdf
Supplementary material:
 2024.semeval-1.117.SupplementaryMaterial.txt
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