Abstract: The objective of the extractive summarization is to extract the important sentences from the original text to form a short summary while retaining the important content of the original text.Query-focused extractive summary model can further satisfy users’ different needs for summary content.Extractive summary model has the natural advantage of ensuring the correctness of summary and the readability of sentences.On this basis,ensuring the relevance and importance of the summary content is the key to the goal of the model.In order to satisfy the relevance of query and ensure the importance of summary content,this paper uses query information as a model study target,creates an extended summary data set based on the title and picture information,an extractive summary model based on knowledge distillation is proposed.In experiments,the pre-training language model BERT is adopted as the encoder,two model training strategies based on knowledge distillation theory are proposed:guided trai-ning and distillation training.Experimental results on CNN/DailyMail,a publicly available data set of news summaries,show that both training methods have achieved significant effects.It is also found that the model based on guiding training could effectively improve the significance of the summary content,while the model based on distillation training achieves the best effect in improving the relevance and significance of the summary.

Key words: Query-focused extractive summarization, Extended data set, BERT, Knowledge distillation