@inproceedings{toney-wails-etal-2026-cards,
title = "{CARDS}: Citation-Aware Research Diffusion Summarization",
author = "Toney-Wails, Autumn and
Quinn, Katherine N. and
Batalis, Steph",
editor = "Accomazzi, Alberto and
Ghosal, Tirthankar and
Grezes, Felix and
Lockhart, Kelly and
Alkan, Atilla Kaan",
booktitle = "Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wasp-1.9/",
pages = "77--90",
ISBN = "979-8-89176-456-9",
abstract = "Tracing how knowledge diffuses through scientific networks reveals how research builds on prior findings, but doing so at scale remains challenging. We present an LLM-based approach, Citation-Aware Research Diffusion Summarization (CARDS), which describes how knowledge diffuses through the scientific landscape. We trace citations from research funded by the U.S. National Institutes of Health through the landscape quantified by CSET{'}s Map of Science cluster dataset to generate Research Diffusion Pathways, and compare LLM-generated summaries based on (1) cluster-level metadata and (2) citation-aware connections between research clusters. We find that CARDS outperforms cluster-based summaries under both human annotation and LLM-as-a-judge evaluation. Our work outlines a methodology for generating research diffusion paths, and for summarizing them with greater specificity."
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<abstract>Tracing how knowledge diffuses through scientific networks reveals how research builds on prior findings, but doing so at scale remains challenging. We present an LLM-based approach, Citation-Aware Research Diffusion Summarization (CARDS), which describes how knowledge diffuses through the scientific landscape. We trace citations from research funded by the U.S. National Institutes of Health through the landscape quantified by CSET’s Map of Science cluster dataset to generate Research Diffusion Pathways, and compare LLM-generated summaries based on (1) cluster-level metadata and (2) citation-aware connections between research clusters. We find that CARDS outperforms cluster-based summaries under both human annotation and LLM-as-a-judge evaluation. Our work outlines a methodology for generating research diffusion paths, and for summarizing them with greater specificity.</abstract>
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%0 Conference Proceedings
%T CARDS: Citation-Aware Research Diffusion Summarization
%A Toney-Wails, Autumn
%A Quinn, Katherine N.
%A Batalis, Steph
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%Y Alkan, Atilla Kaan
%S Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-456-9
%F toney-wails-etal-2026-cards
%X Tracing how knowledge diffuses through scientific networks reveals how research builds on prior findings, but doing so at scale remains challenging. We present an LLM-based approach, Citation-Aware Research Diffusion Summarization (CARDS), which describes how knowledge diffuses through the scientific landscape. We trace citations from research funded by the U.S. National Institutes of Health through the landscape quantified by CSET’s Map of Science cluster dataset to generate Research Diffusion Pathways, and compare LLM-generated summaries based on (1) cluster-level metadata and (2) citation-aware connections between research clusters. We find that CARDS outperforms cluster-based summaries under both human annotation and LLM-as-a-judge evaluation. Our work outlines a methodology for generating research diffusion paths, and for summarizing them with greater specificity.
%U https://aclanthology.org/2026.wasp-1.9/
%P 77-90
Markdown (Informal)
[CARDS: Citation-Aware Research Diffusion Summarization](https://aclanthology.org/2026.wasp-1.9/) (Toney-Wails et al., WASP 2026)
ACL
- Autumn Toney-Wails, Katherine N. Quinn, and Steph Batalis. 2026. CARDS: Citation-Aware Research Diffusion Summarization. In Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications, pages 77–90, Hengqin, Zhuhai, China. Association for Computational Linguistics.