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arXiv · 2309.01455

NumHG: A Dataset for Number-Focused Headline Generation

Jian-Tao Huang·Chung-Chi Chen·Hen-Hsen Huang·Hsin-Hsi Chen
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Abstract

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.

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Jian-Tao Huang, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen. 2023-09-04. NumHG: A Dataset for Number-Focused Headline Generation. https://arxiv.org/abs/2309.01455

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