0 Introduction

The problem of information overload caused by the massive growth of text data in the digital age, as well as the inability of manual summarization methods to meet user needs. In addition, a deep learning based generative text summarization method was introduced, which automatically analyzes text content, extracts key information, and compresses it into concise summaries. This can significantly reduce user time and effort, improve information processing efficiency and accuracy.

1 Text Summary Task

1. Definition and Objectives of Text Abstract

2. DL based ATS model framework

1. Application of Encoder Decoder Model Framework in ATS

The main problems and solutions faced by ATS

1. Methods for dealing with OOV word and repetition problems, including using attention mechanisms, encoder decoder framework based methods, and pointer generator based strategies.

4 Research Difficulties

The difficulties of generating text summaries based on deep learning include information omission, redundancy, and semantic consistency issues during the generation of long text summaries. ATS needs to maintain structural consistency and logical coherence during the generation process, as well as semantic understanding and factual accuracy issues. In addition, the generative summarization model also needs to have a broad knowledge background and common sense reasoning ability, as well as integrate relevant knowledge and common sense to ensure consistency between the generated summary and domain knowledge and common sense. Finally, this chapter also discusses the importance of finding reasonable evaluation indicators and methods.

5 Future research directions

The future research directions of personalized summary generation based on deep learning, introducing richer external knowledge, adopting flexible termination criteria, and cross language or low resource language summaries. These directions can further enhance the factual accuracy of generated summaries, and enable the summary generation system to adapt to different scenarios and domains, as well as how to generate personalized summaries that meet user needs. Meanwhile, key unresolved issues in current research were discussed, such as how to train cross language or low resource language summarization models.

6 Conclusion

The current status of automatic text summarization technology, especially generative text summarization models based on deep learning. These models include the overall framework, model design details, training strategies, etc. In addition, the advantages and disadvantages of such methods were summarized, providing suggestions for researchers to choose suitable frameworks and models, and offering new perspectives and inspirations for future research and application of automatic text summarization.