This study participates in the ROCLING-2025 shared task on Chinese dimensional sentiment analysis for medical self-reflection texts. Dimensional Sentiment Analysis (DSA) represents emotions as continuous dimensions, such as valence (positive to negative) and arousal (calm to excited), providing finer-grained representations compared to traditional categorical approaches, which are suitable for applications in mental health monitoring and risk assessment. We use large language models (LLMs) to extract contextual embedding vectors, which are then fed into regression models, such as Support Vector Regression (SVR), for valence-arousal prediction. The training data consists of the Chinese EmoBank dataset (2,954 general-domain samples), the validation data is a Medical Self-Reflection Corpus Dataset (994 samples), and the test data is another Medical Self-Reflection Corpus Dataset (1,541 samples). Experimental results show that the SVR model with DeepSeek embeddings performs best. Multi-model ensemble learning further improves performance to 0.463 valence MAE, 0.759 arousal MAE, 0.805 valence PCC, and 0.608 arousal PCC. This approach shows the potential of multi-model fusion in DSA for biomedical applications, facilitating the development of non-intrusive mental health assessment tools.

Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0.447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task.