Document collections of various domains, e.g., legal, medical, or financial,
often share some underlying collection-wide structure, which captures
information that can aid both human users and structure-aware models. We
propose to identify the typical structure of document within a collection,
which requires to capture recurring topics across the collection, while
abstracting over arbitrary header paraphrases, and ground each topic to
respective document locations. These requirements pose several challenges:
headers that mark recurring topics frequently differ in phrasing, certain
section headers are unique to individual documents and do not reflect the
typical structure, and the order of topics can vary between documents.
Subsequently, we develop an unsupervised graph-based method which leverages
both inter- and intra-document similarities, to extract the underlying
collection-wide structure. Our evaluations on three diverse domains in both
English and Hebrew indicate that our method extracts meaningful collection-wide
structure, and we hope that future work will leverage our method for
multi-document applications and structure-aware models.

文档收集的典型结构与循环主题之间的关系是核心问题，提出了一种基于图的方法来提取整个收集的结构，并希望未来的工作能够利用这种方法进行多文档应用和结构感知模型的研究。

利用整体相似性进行无监督文档结构提取

Leveraging Collection-Wide Similarities for Unsupervised Document  Structure Extraction

A standard measure of the influence of a research paper is the number of
times it is cited. However, papers may be cited for many reasons, and citation
count offers limited information about the extent to which a paper affected the
content of subsequent publications. We therefore propose a novel method to
quantify linguistic influence in timestamped document collections. There are
two main steps: first, identify lexical and semantic changes using contextual
embeddings and word frequencies; second, aggregate information about these
changes into per-document influence scores by estimating a high-dimensional
Hawkes process with a low-rank parameter matrix. We show that this measure of
linguistic influence is predictive of $\textit{future}$ citations: the estimate
of linguistic influence from the two years after a paper's publication is
correlated with and predictive of its citation count in the following three
years. This is demonstrated using an online evaluation with incremental
temporal training/test splits, in comparison with a strong baseline that
includes predictors for initial citation counts, topics, and lexical features.

该研究提出了一种基于语言影响力的新方法来量化文献对后续出版物内容的影响程度，并通过测量高维度的 Hawkes 过程来预测未来引用次数。

从短期语言影响预测长期引用

Predicting Long-Term Citations from Short-Term Linguistic Influence

Exploration and analysis of potential data sources is a significant challenge
in the application of NLP techniques to novel information domains. We describe
HARE, a system for highlighting relevant information in document collections to
support ranking and triage, which provides tools for post-processing and
qualitative analysis for model development and tuning. We apply HARE to the use
case of narrative descriptions of mobility information in clinical data, and
demonstrate its utility in comparing candidate embedding features. We provide a
web-based interface for annotation visualization and document ranking, with a
modular backend to support interoperability with existing annotation tools. Our
system is available online at this https URL

本研究探讨了自然语言处理技术在新型信息领域应用中的数据来源和分析问题，提出了一种名为 HARE 的系统，支持文档集合中相关信息的筛选与排序，并提供了后期处理和质量分析工具，以用于模型开发和调试，在移动信息的临床数据中应用 HARE 系统，比较了几种候选嵌入特征，提供了基于 Web 的界面和可视化工具，支持与现有注释工具的互操作。