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ARXIV:2603.16131 · SCIENTIFIC SUMMARIZATION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2603.16131SCIENTIFIC SUMMARIZATIONSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era.
Opportunity summary
Pain SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era. While LLMs are increasingly adopted for summarization, existing benchmarks remain limited in scale, target only…
The explosive growth of AI research has created unprecedented information overload, increasing the demand for scientific summarization at multiple levels of granularity beyond traditional abstracts. While LLMs are increasingly adopted for summarization, existing benchmarks…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Our code and dataset are publicly available on GitHub (https://github.com/janghana/SciZoom) and Hugging Face (https://huggingface.co/datasets/hanjang/SciZoom), respectively.
Scientific Summarization moved forward this cycle; last verified April 2026. Public score 9.0/10.
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SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era.
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10.48550/arXiv.2603.16131SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era.
Abstract
The explosive growth of AI research has created unprecedented information overload, increasing the demand for scientific summarization at multiple levels of granularity beyond traditional abstracts. While LLMs are increasingly adopted for summarization, existing benchmarks remain limited in scale, target only a single granularity, and predate the LLM era. Moreover, since the release of ChatGPT in November 2022, researchers have rapidly adopted LLMs for drafting manuscripts themselves, fundamentally transforming scientific writing, yet no resource exists to analyze how this writing has evolved. To bridge these gaps, we introduce SciZoom, a benchmark comprising 44,946 papers from four top-tier ML venues (NeurIPS, ICLR, ICML, EMNLP) spanning 2020 to 2025, explicitly stratified into Pre-LLM and Post-LLM eras. SciZoom provides three hierarchical summarization targets (Abstract, Contributions, and TL;DR) achieving compression ratios up to 600:1, enabling both multi-granularity summarization research and temporal mining of scientific writing patterns. Our linguistic analysis reveals striking shifts in phrase patterns (up to 10x for formulaic expressions) and rhetorical style (23% decline in hedging), suggesting that LLM-assisted writing produces more confident yet homogenized prose. SciZoom serves as both a challenging benchmark and a unique resource for mining the evolution of scientific discourse in the generative AI era. Our code and dataset are publicly available on GitHub (https://github.com/janghana/SciZoom) and Hugging Face (https://huggingface.co/datasets/hanjang/SciZoom), respectively.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Commercial
Export
Preparing verified analysis
Dimensions overall score 9.0
PROBLEM
SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era. While LLMs are increasingly adopted for summarization, existing benchmarks remain limited in scale, target only a single granularity, and...
METHOD
The explosive growth of AI research has created unprecedented information overload, increasing the demand for scientific summarization at multiple levels of granularity beyond traditional abstracts. While LLMs are increasingly adopted for summarization, existing benchmarks remai...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Our code and dataset are publicly available on GitHub (https://github.com/janghana/SciZoom) and Hugging Face (https://huggingface.co/datasets/hanjang/SciZoom), respectively.
WHY NOW
Scientific Summarization moved forward this cycle; last verified April 2026. Public score 9.0/10.
we introduce SciZoom, a benchmark comprising 44,946 papers from four top-tier ML venues (NeurIPS, ICLR, ICML, EMNLP) spanning 2020 to 2025, explicitly stratified into Pre-LLM and Post-LLM eras.
Implication not extracted yet.
partial
SciZoom provides three hierarchical summarization targets (Abstract, Contributions, and TL;DR) achieving compression ratios up to 600:1
Implication not extracted yet.
partial
Our linguistic analysis reveals striking shifts in phrase patterns (up to 10x for formulaic expressions) and rhetorical style (23% decline in hedging)
Implication not extracted yet.
partial
suggesting that LLM-assisted writing produces more confident yet homogenized prose
Implication not extracted yet.
partial
existing benchmarks remain limited in scale, target only a single granularity, and predate the LLM era
Implication not extracted yet.
partial
no resource exists to analyze how this writing has evolved
Implication not extracted yet.
partial
Dataset limited to four ML venues, may not generalize to other scientific fields
Implication not extracted yet.
partial
enabling both multi-granularity summarization research and temporal mining of scientific writing patterns
Implication not extracted yet.
partial
we introduce SciZoom, a benchmark comprising 44,946 papers from four top-tier ML venues (NeurIPS, ICLR, ICML, EMNLP) spanning 2020 to 2025
Directly stated in the abstract with specific numbers and venue names.
partial
SciZoom provides three hierarchical summarization targets (Abstract, Contributions, and TL;DR)
Explicitly mentioned in the abstract as the summarization targets provided by SciZoom.
partial
Our linguistic analysis reveals striking shifts in phrase patterns (up to 10x for formulaic expressions) and rhetorical style (23% decline in hedging), suggesting that LLM-assisted writing produces more confident yet homogenized prose.
The abstract states this finding and provides a specific percentage for hedging decline, indicating a direct result of their analysis.
partial
Dataset limited to four ML venues, may not generalize to other scientific fields
This is explicitly listed as a caveat in the provided analysis.
partial
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SciZoom is a comprehensive benchmark for hierarchical scientific summarization, analyzing the evolution of scientific writing in the LLM era.
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Scientific Summarization
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Commercial read
9.0/10 public viability
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Technical feasibility
partial
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Gaps
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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