The field of table-to-text generation and schema management is experiencing significant growth, with a focus on improving the accuracy and interpretability of generated text. Researchers are exploring new approaches to incorporate subjectivity and intermediate representations into table-to-text generation pipelines, enabling the creation of more informative and engaging text. Additionally, there is a shift towards structured representations and component-based retrieval architectures to improve the scalability and reliability of schema management and text-to-SQL systems. Noteworthy papers include: Setting The Table with Intent, which proposes a novel approach for intent-aware schema generation and editing. Ta-G-T, which introduces a pipeline for capturing subjectivity in table-to-text generation via RDF graphs. StructText, which presents a synthetic table-to-text approach for benchmark generation with multi-dimensional evaluation. Beyond Natural Language Plans, which proposes a paradigm shift to structured representations for query-focused table summarization. RASL, which introduces a retrieval augmented schema linking approach for massive database text-to-SQL.