## 1 Introduction  
The aim of so-called key event detection is to obtain a set of clusters in a corpus about a certain topic, with each cluster containing a set of articles describing the same event. The event context is a process of generating event descriptions for each key event and arranging them by time of occurrence. With the rapid development of computer and network technology, an exponentially growing, complex, and massive amount of online data and information has emerged. As a result, people often only see partial information, and it is thus difficult to obtain a comprehensive and quick overview of the big picture. Key event detection can be involved in applications across different modalities, such as video summarization [1] and augmented reality [2]. Accordingly, the search for more advanced techniques to detect events and their contexts, enabling the rapid and accurate detection of key events in large-scale data and the timely observation of the context of such key events [3], has become a growing subject for many researchers. News events evolve rapidly and are variable in nature [4]. It is therefore difficult to obtain large amounts of annotated data. Meanwhile, the models obtained on the basis of training data are less portable. Thus, the use of unsupervised methods such as clustering for the detection of key events has also become a trend. For example, [PERSON] et al. [5] constructeda KeyGraph and then used a community discovery algorithm for clustering. A Burst Information Network (BINet) based event detection model, the Time-aware Hierarchical Bayesian Model (TaHBM), was proposed by [PERSON] et al. [6]. [PERSON] et al. proposed an unsupervised key event detection model, EvMine [7]. Both of the above methods incorporate time information. Usually, a key event is discussed in articles with similar content and is published within a certain timeframe. After an important news event occurs, it is widely discussed in the following days, but the discussion gradually fades as the time frame expands. This shows that time information can serve as an indicative factor for describing different key events in news. Integrating time information into clustering algorithms can improve their clustering effectiveness. Typically, a news article includes not only the text of the news itself, but also the time of publication. This publishing pattern provides a natural convenience for utilizing time information. Among the aforementioned models, EvMine is a more effective method for event detection and has been successfully applied in detecting key events. However, EvMine has the following limitations: (1) The practice of artificially assigning weights to edges between graph nodes based on experience is highly subjective and lacks generalizability. (2) The deep semantic information contained in the nodes of the original peak phrase graph is not considered. The nodes are clustered in the original peak phrase graph, and the clustering results are often unsatisfactory. (3) As EvMine focuses on key event detection, it lacks automatic identification of the key event context. As a result, there is a need to improve the utilization of time information in constructing the connected edges of a graph to obtain high-quality semantic feature information for the nodes and enable automatic identification of the context of key events. These three key points are crucial for the study of key event detection and event context. To address the above issues, inspired by the EvMine model, this paper proposes a method for detection of key events in news documents and identification of the events' context based on graphic convolving, clustering, and summarizing (GCS). The work presented in this paper mainly includes: (1) The union set of the semantic embedding of high-quality phrases extracted from the news corpus and the embedded release times of the documents to which the phrases belong are used as the nodes of the graph. The nodes are connected based on the news release rules in reality, thereby constructing an initial graph of high-quality phrases.