The field of natural language processing (NLP) is experiencing significant advancements in its application to scientific research. One notable trend is the increasing use of NLP to facilitate materials discovery and other stages of the battery life cycle. Additionally, there is a growing interest in using language models to automatically generate descriptive and human-readable labels for clusters of scientific documents, which can improve the efficiency and accuracy of bibliometric workflows. Another area of innovation is the development of efficient topic extraction methods, such as graph-based labeling, which can offer effective alternatives to deep models. These advancements have the potential to revolutionize the way researchers work with large amounts of textual data and can lead to significant breakthroughs in various fields of science. Noteworthy papers include:

• A systematic survey of NLP in battery life cycle, which introduces a novel technical language processing framework and highlights the emergence of new NLP tasks in the battery domain.
• A study on using language models to label clusters of scientific documents, which provides a formal descriptive labeling framework and demonstrates the effectiveness of language models in generating descriptive labels.
• A paper on efficient topic extraction via graph-based labeling, which proposes a lightweight alternative to deep models and achieves consistently better results than traditional benchmarks.
• The introduction of the ARETE R package, which automates data extraction of species occurrences using large language models and has the potential to significantly improve conservation initiatives.