In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913037590462464 |
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| author | Arnaout, Hiba Sternlicht, Noy Hope, Tom Gurevych, Iryna |
| author_facet | Arnaout, Hiba Sternlicht, Noy Hope, Tom Gurevych, Iryna |
| contents | Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss the nuanced ways a paper contributes to its field. In this work, we propose a new task: generating nuanced, expressive, and time-aware impact summaries that capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents. We introduce an evaluation framework tailored to this task, showing moderate to strong human correlation on subjective metrics such as insightfulness. Expert feedback from professors reveals a strong interest in these summaries and suggests future improvements. Data and code are made available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis Arnaout, Hiba Sternlicht, Noy Hope, Tom Gurevych, Iryna Digital Libraries Artificial Intelligence Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss the nuanced ways a paper contributes to its field. In this work, we propose a new task: generating nuanced, expressive, and time-aware impact summaries that capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents. We introduce an evaluation framework tailored to this task, showing moderate to strong human correlation on subjective metrics such as insightfulness. Expert feedback from professors reveals a strong interest in these summaries and suggests future improvements. Data and code are made available. |
| title | In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis |
| topic | Digital Libraries Artificial Intelligence |
| url | https://arxiv.org/abs/2505.14838 |