Automatic summarization model based on clustering algorithm

Extractive document summary (EDS) is usually seen as a sequence labeling task, which the summary is formulated by sentences from the original document. However, the selected sentences usually are high redundancy in semantic space, so that the composed summary are high semantic redundancy. To alleviate this problem, we propose a model to reduce the semantic redundancy of summary by introducing the cluster algorithm in semantic space. We evaluate our model and perform significance testing on the CNN/DailyMail datasets compare with baselines.