arXiv cs.AI· Ibrahim Al Azher, Hamed Alhoori·· 13 小时前AI 评分18
FutureLens：学术文本与未来工作引用中的纵向主题模式
FutureLens: Longitudinal Topic Patterns in Scholarly Text and Future-Work References
AI 导读
FutureLens 基于 2012–2022 年 5,655 篇 ACL 论文（其中 4,005 篇含未来工作引用文本）考察纵向主题测量问题。NMF 的平均归一化点互信息为 0.1387、高于 LDA 的 0.1280，且分配稳定性更高；LDA 关键词多样性更高（0.8900 对 0.7750）。
正文 · 原文
Abstract:Future-work statements offer a record of the questions researchers choose to leave open. Using them to describe a field, however, requires separating thematic change from changes in document coverage, topic assignment, and scholarly writing. FutureLens examines these measurement issues in a recovered snapshot of 5,655 ACL papers published during 2012--2022, including 4,005 with supplied future-work reference text. We compare non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA), aggregate topic presence at the paper level, and examine normalized prevalence, exploratory trend statistics, assignment stability, and agreement between input-derived text and references. Across available seeds, NMF has higher mean normalized pointwise mutual information (0.1387 versus 0.1280) and higher pairwise assignment stability, while LDA has greater keyword diversity (0.8900 versus 0.7750). Seed-zero results show increasing prevalence of broad model-training and question-related vocabulary, alongside substantial citation and document-structure content. Mean input/reference topic-set overlap is 0.5191 for NMF and 0.4924 for LDA; temporal rank agreement varies widely and does not imply agreement in prevalence levels. These findings support a descriptive account of the available corpus, with explicit qualifications for missing references, incomplete runs, and unverified reference provenance. The contribution is an empirical measurement study of longitudinal scholarly signals, not a new topic-modeling algorithm or a forecasting system.
| Comments: | 10 pages, Accepted at AI4SciSci 2026 (3rd International Workshop on Artificial Intelligence for the Science of Science), co-located with ACM/IEEE-CS JCDL 2026 |
| Subjects: | Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.28656 [cs.DL] |
| (or arXiv:2610.28656v1 [cs.DL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28656 arXiv-issued DOI via DataCite |
Submission history
From: Ibrahim Al Azhar [view email]
[v1]
Fri, 9 Oct 2026 09:50:18 UTC (45 KB)
来源：arXiv cs.AI · arxiv.org