Abstract: Automatic summarization technology refers to providing users with a concise text description by compressing and refining the original text while retaining the core idea of document.Usually,the traditional method only considers the shallow textual semantic information and neglects the guiding role of the structure information such as the primary and secondary relations in core sentences extraction.Therefore,this paper proposes an automatic summarization method based on the primary and secondary relation feature.This method utilizes the neural network to construct a single document extractive summarization model based on primary and secondary relation feature.The Bi-LSTM neural network model is used to encode the sentence information and primary and secondary relation information,and the LSTM neural network is utilized to summarize the encoded information.Experimental results show that the proposed method has a significant improvement in accuracy,stability and the ROUGE evaluation index compared with the current mainstream single document extractive summarization methods.

Key words: Extractive summarization, LSTM, Natural language processing, Neural network, Primary and secondary relation