Scientific charts are essential tools for effectively communicating research
findings, serving as a vital medium for conveying information and revealing
data patterns. With the rapid advancement of science and technology, coupled
with the advent of the big data era, the volume and diversity of scientific
research data have surged, leading to an increase in the number and variety of
charts. This trend presents new challenges for researchers, particularly in
efficiently and accurately generating appropriate titles for these charts to
better convey their information and results. Automatically generated chart
titles can enhance information retrieval systems by providing precise data for
detailed chart classification. As research in image captioning and text
summarization matures, the automatic generation of scientific chart titles has
gained significant attention. By leveraging natural language processing,
machine learning, and multimodal techniques, it is possible to automatically
extract key information from charts and generate accurate, concise titles that
better serve the needs of researchers. This paper presents a novel approach to
scientific chart title generation, demonstrating its effectiveness in improving
the clarity and accessibility of research data.

本文提出了一种新的科学图表标题生成方法，通过自然语言处理、机器学习和多模态技术，从图表中提取关键信息并生成准确简洁的标题，以提高研究数据的清晰度和可访问性。

通过跨模态学习增强科学图像标题

Enhancing Scientific Figure Captioning Through Cross-modal Learning

Accessibility of research data is critical for advances in many research
fields, but textual data often cannot be shared due to the personal and
sensitive information which it contains, e.g names or political opinions.
General Data Protection Regulation (GDPR) suggests pseudonymization as a
solution to secure open access to research data, but we need to learn more
about pseudonymization as an approach before adopting it for manipulation of
research data. This paper outlines a research agenda within pseudonymization,
namely need of studies into the effects of pseudonymization on unstructured
data in relation to e.g. readability and language assessment, as well as the
effectiveness of pseudonymization as a way of protecting writer identity, while
also exploring different ways of developing context-sensitive algorithms for
detection, labelling and replacement of personal information in unstructured
data. The recently granted project on pseudonymization Grandma Karl is 27 years
old addresses exactly those challenges.

本文概述了数据伪装领域的研究议程，即关于伪装处理对非结构化数据的影响，如可读性和语言评估等方面的研究需求，以及伪装作为保护作者身份的有效方式以及在非结构化数据中检测、标记和替换个人信息的不同上下文敏感算法的发展方法。刚获得的伪装项目 “Grandma Karl is 27 years old” 正好解决了这些挑战。