2023/09/04 by Jian-Tao Huang, Huang, Jian-Tao, Chung-Chi Chen +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Mathematics, Computing, and Information Processing #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2309.01455
openalex publication_date 2023/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Headline generation, a key task in abstractive summarization, strives to condense a full-length article into a succinct, single line of text. Notably, while contemporary encoder-decoder models excel based on the ROUGE metric, they often falter when it comes to the precise generation of numerals in headlines. We identify the lack of datasets providing fine-grained annotations for accurate numeral generation as a major roadblock. To address this, we introduce a new dataset, the NumHG, and provide over 27,000 annotated numeral-rich news articles for detailed investigation. Further, we evaluate five well-performing models from previous headline generation tasks using human evaluation in terms of numerical accuracy, reasonableness, and readability. Our study reveals a need for improvement in numerical accuracy, demonstrating the potential of the NumHG dataset to drive progress in number-focused headline generation and stimulate further discussions in numeral-focused text generation.