Abstract: Extractive text summarization is a method of extracting key text fragments from the input text to serve as the summary. In order to solve the problem of requiring sentence-level labels during training, extractive text summarization is modeled as a Q-Learning problem and DQN(Deep Q-Network) to learn the Q value function. The document representation method is crucial for the quality of the generated summarization. To effectively represent the document, we adopt a hierarchical document representation method, which uses Bidirectional Encoder Representations from Transformers to obtain sentence-level vector representation and uses Transformer to obtain document-level vector representation. The decoder considers the sentence information enrichment, saliency, position, and redundancy degree between a sentence and the current summarization. This method does not require sentence-level labels when extracting sentences, which significantly reduces workload. Experiments on CNN( Cable News Network) / DailyMail data sets show that, compared with other extraction models, this model achieves the best Rouge-L(38. 35) and comparable Rouge-1(42. 07) and Rouge-2(18. 32) performance.

Key words: extractive text summarization')">

extractive text summarization, bidirectional encoder representations from transformers ( BERT), transformer, deep Q-learning (DQN)