Temporal Twitter summarization is an important sub-task of text summarization, which aims to extract a concise tweet set with time, goes from a huge Twitter stream.It helps users quickly understand a specific event.As one of the most popular social media platforms, the explosive growth of Twitter information makes it difficult for users to find reliable and useful information.As tweets are short and highly unstructured, it makes traditional document summarization methods difficult to handle Twitter data.Meanwhile, Twitter also provides rich temporal-social context more than texts, bringing new opportunities.This paper considers Twitter stream as a kind of signal, and proposes a novel temporal Twitter summarization method by modeling macro-micro temporal context and social context through analyzing the complex noises hidden in signal.First, time points of hot sub-events are detected by modeling temporal context globally with wavelet analysis.Second, a novel random walk model is built on graph based unsupervised Twitter summarization framework, integrating both local temporal context and social user authority to generate summary for each sub-event time point.To evaluate the proposed framework, a real-world Twitter dataset, including expert time point and summary, is manually labeled.Experimental results show that wavelet analysis during hot sub-event time point detection and temporal-social context in Twitter summarization are both effective.