CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912251653390336 |
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| author | Liu, Javin Vats, Aryan He, Zihao |
| author_facet | Liu, Javin Vats, Aryan He, Zihao |
| contents | The rapid expansion of scientific literature in computer science presents challenges in tracking research trends and extracting key insights. Existing datasets provide metadata but lack structured summaries that capture core contributions and methodologies. We introduce CS-PaperSum, a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences, enriched with AI-generated structured summaries using ChatGPT. To assess summary quality, we conduct embedding alignment analysis and keyword overlap analysis, demonstrating strong preservation of key concepts. We further present a case study on AI research trends, highlighting shifts in methodologies and interdisciplinary crossovers, including the rise of self-supervised learning, retrieval-augmented generation, and multimodal AI. Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery, providing a valuable resource for researchers, policymakers, and scientific information retrieval systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_20582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers Liu, Javin Vats, Aryan He, Zihao Information Retrieval The rapid expansion of scientific literature in computer science presents challenges in tracking research trends and extracting key insights. Existing datasets provide metadata but lack structured summaries that capture core contributions and methodologies. We introduce CS-PaperSum, a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences, enriched with AI-generated structured summaries using ChatGPT. To assess summary quality, we conduct embedding alignment analysis and keyword overlap analysis, demonstrating strong preservation of key concepts. We further present a case study on AI research trends, highlighting shifts in methodologies and interdisciplinary crossovers, including the rise of self-supervised learning, retrieval-augmented generation, and multimodal AI. Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery, providing a valuable resource for researchers, policymakers, and scientific information retrieval systems. |
| title | CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2502.20582 |