Most of the current extractive summarization methods mainly focus on the representation and extraction of summary sentences,and tend to ignore the adequacy of text feature representation.In order to solve this problem,an extractive summarization method was proposed.Firstly,on the basis of abstract tasks,an extractive summarization model (MLHIN) based on metric learning and hierarchical inference was proposed.Secondly,the model was evaluated and tested on the English CNN/DailyMail dataset.Finally,the test results of the model on the dataset are verified.The results show that the proposed model has significantly higher scores than other models on Rouge-1,Rouge-2 and Rouge-L,which are 0.84%,1.29% and 2.43% higher than the Lead-3 model respectively.After replacing the metric loss metric and the sentence encoder with other modules,it can be seen that the performance of the model has declined to varying degrees,which proves the effectiveness of the proposed hierarchical inference network and metric loss.The algorithm can improve the ability of model to capture long-distance dependency,enhance the ability of model to distinguish summary sentences from non-summary sentences,and effectively improve the performance of the extractive summarization methods.