Bridging Numerical Reasoning and Headline Generation for Enhanced Language Models (2024)

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.

## introduction in the dynamic domain of digital media, the synthesis of scientific rigor and creative flair in headline generation is paramount for capturing audience interest while maintaining accuracy. yet, a persistent challenge arises in integrating numerical data into these headlines with precision. conventional methods often fall short, either by overlooking crucial numerical insights or sacrificing clarity.consider the task of distilling information from source material, where existing techniques frequently neglect the nuances of numerical discourse-a critical shortfall, particularly in fields such as finance. this challenge extends to the domain of natural language processing (nlp), where computational systems strive to comprehend and generate human language seamlessly.our research addresses this challenge through an innovative methodological approach. by leveraging advanced language models like bert, roberta, and t5, we aim to advance computational linguistics, particularly in reconciling textual narratives with numerical data.furthermore, we introduce the flan-t5 model, which integrates external contributions and proprietary datasets to enhance headline generation capabilities. through systematic comparative analysis, our study validates the efficacy of our approach in overcoming challenges related to numerical integration and headline creation. ## conclusion our research presents a significant stride in advancing numerical reasoning within the domain of news headline generation. the thorough evaluation of transformer models, including flan-t5, distilroberta, and t5 variants, showcased remarkable improvements in accuracy for filling blank headlines with hidden numbers. flan-t5 (two steps) particularly stood out with a commendable 90.2% accuracy, demonstrating exceptional competence in arithmetic operations and handling complex mathematical expressions. additionally, the nuanced understanding of numerals displayed by t5 models in subtask 2 underscores their efficacy in generating contextually relevant headlines. these findings collectively contribute valuable insights into the evolving landscape of natural language processing, especially in tasks involving numerical reasoning and headline creation.