Citation field learning is to segment a citation string into fields of
interest such as author, title, and venue. Extracting such fields from
citations is crucial for citation indexing, researcher profile analysis, etc.
User-generated resources like academic homepages and Curriculum Vitae, provide
rich citation field information. However, extracting fields from these
resources is challenging due to inconsistent citation styles, incomplete
sentence syntax, and insufficient training data. To address these challenges,
we propose a novel algorithm, CIFAL (citation field learning by anchor
learning), to boost the citation field learning performance. CIFAL leverages
the anchor learning, which is model-agnostic for any Pre-trained Language
Model, to help capture citation patterns from the data of different citation
styles. The experiments demonstrate that CIFAL outperforms state-of-the-art
methods in citation field learning, achieving a 2.83% improvement in
field-level F1-scores. Extensive analysis of the results further confirms the
effectiveness of CIFAL quantitatively and qualitatively.

提出了一种名为 CIFAL 的新算法，通过锚学习来提高引文字段学习性能，并在引文字段学习中取得了 2.83% 的 F1 分数的提升。

一种用于引文领域学习的锚点学习方法

An Anchor Learning Approach for Citation Field Learning

Previous work indicates that over the past 20 years, the highest quality work
have been published in an increasingly diverse and larger group of journals. In
this paper we examine whether this diversification has also affected the
handful of elite journals that are traditionally considered to be the best. We
examine citation patterns over the past 40 years of 7 long-standing
traditionally elite journals and 6 journals that have been increasing in
importance over the past 20 years. To be among the top 5% or 1% cited papers,
papers now need about twice as many citations as they did 40 years ago. Since
the late 1980s and early 1990s elite journals have been publishing a decreasing
proportion of these top cited papers. This also applies to the two journals
that are typically considered as the top venues and often used as bibliometric
indicators of "excellence", Science and Nature. On the other hand, several new
and established journals are publishing an increasing proportion of most cited
papers. These changes bring new challenges and opportunities for all parties.
Journals can enact policies to increase or maintain their relative position in
the journal hierarchy. Researchers now have the option to publish in more
diverse venues knowing that their work can still reach the same audiences.
Finally, evaluators and administrators need to know that although there will
always be a certain prestige associated with publishing in "elite" journals,
journal hierarchies are in constant flux so inclusion of journals into this
group is not permanent.

本研究研究了过去 40 年 7 个传统的精英期刊和近 20 年变得越来越重要的 6 个期刊的学术引用模式，发现过去这些顶尖论文在这些传统的精英期刊中所占比例逐渐降低，而新近建立的期刊却逐渐成为顶尖论文的主要出版载体。这些变化为出版方、研究者和管理员带来新的挑战和机遇，同时也提示了 “精英期刊” 的地位是不断变化的。

高水平期刊是否在衰退？

Are elite journals declining?

To comprehend the multipartite organization of large-scale biological and
social systems, we introduce a new information theoretic approach that reveals
community structure in weighted and directed networks. The method decomposes a
network into modules by optimally compressing a description of information
flows on the network. The result is a map that both simplifies and highlights
the regularities in the structure and their relationships. We illustrate the
method by making a map of scientific communication as captured in the citation
patterns of more than 6000 journals. We discover a multicentric organization
with fields that vary dramatically in size and degree of integration into the
network of science. Along the backbone of the network -- including physics,
chemistry, molecular biology, and medicine -- information flows
bidirectionally, but the map reveals a directional pattern of citation from the
applied fields to the basic sciences.

新的信息理论方法揭示加权和定向网络中的社区结构，例如 6000 多种期刊的引用模式，发现多中心组织以及学科之间的定向引用模式。