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
Ibrahim Al Azher · Hamed Alhoori
中文摘要
FutureLens考察由2012—2022年发布的5,655篇ACL论文构成的恢复快照，其中4,005篇附有未来工作引用文本。研究比较非负矩阵分解(NMF)与潜在Dirichlet分配(LDA)在论文级主题汇总、归一化流行度、探索性趋势统计、分配稳定性及输入文本与引用文本一致性方面的差异。NMF的平均归一化逐点互信息为0.1387，高于LDA的0.1280，且成对分配稳定性更高；LDA关键词多样性为0.8900，高于NMF的0.7750。种子为零的结果显示，广泛模型训练与问题相关词汇日益常见，同时存在大量引用及文档结构内容。输入与引用的平均主题集合重叠率在NMF和LDA中分别为0.5191和0.4924；时间排序一致性差异较大，且不代表流行度水平一致。结论适用于现有语料的描述性分析，并受缺失引用、未完成运行及引用来源未验证等限制。该工作是纵向学术信号的实证测量研究，而非新主题建模算法或预测系统。
关键要点
- 01研究区分主题变化与文献覆盖、主题分配及写作风格变化。
- 02基于2012—2022年5,655篇ACL论文，其中4,005篇含未来工作文本。
- 03NMF主题一致性更高，LDA关键词多样性更高。
- 04输入文本与引用的主题重叠率未达到完全一致。
- 05结果受缺失引用、不完整运行和来源未验证限制。
解读
尚无解读。
原始英文摘要
arXiv:2610.28656v1 Announce Type: cross 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.