Existing emotion datasets have supported a wide range of NLP tasks, but most are static resources that capture language use only at the time of their creation. As a result, they cannot represent how emotional meanings shift in response to cultural and social change. To address this limitation, we present NAIST LIFE STORY, a seven-year collection of Japanese emotion-related episodes that reflect contemporary topics across multiple years. Since 2017, 1,000 crowdsourced participants per quarter have written short texts describing personal experiences associated with seven emotions: anger, anxiety, disgust, trust, joy, sadness, and surprise. The dataset currently spans 28 periods and includes gender and age information for each participant. Analyses reveal systematic differences in text length and lexical diversity across emotions, as well as clear temporal trends linked to major events such as the COVID-19 pandemic. A preliminary experiment with a large language model shows that using this dataset as contextual evidence improves time-aware emotion inference, demonstrating its value for studying the evolving relationship between emotion and language.

Medical research is highly specialized, making it difficult for patients and general readers to understand recent findings.Traditionally, text simplification, replacing technical terms with more accessible expressions, has been employed. However, this approach alone is limited in addressing a lack of background knowledge and often results in the loss of important information.Therefore, this study defines “rewriting for non-experts” as a rewriting process that, in addition to simplification, supplements essential background knowledge such as the significance of the research and reasons it is needed and proposes a method for implementing this process using large language models (LLMs).To verify the effectiveness of the proposed approach, a quantitative evaluation using automatic metrics was conducted. The results showed that the method combining the guidelines for human text creation with few-shot examples of reference texts achieved the highest scores.The expansion of the guidelines is planned as part of future work to enable the rewriting of scientific and technological information in a form that is accessible to a broader audience.