# An Abstract Summarization Method Combining Global Topics
## 2 Related Works  
The mainstream automatic summarization methods can be divided into two types: extractive and abstractive. Extractive methods involve extracting words and sentences and other semantic units from the original text. Representative extractive methods include semantic-information-based methods [8] and structural-information-based methods [9; 10]. Abstractive summarization methods are closer to manual summarization, which restates the original text with words, sentences, and phrases that are different from the original text. It is a challenge to the model's ability of understanding, representing, and generating. In recent years, the application of deep neural networks and attention mechanisms in machine translation research has promoted the research of abstractive summarization methods with an encoder-decoder structure. In 2015, [PERSON] et al. [5] first introduced an encoder-decoder structure and attention mechanism to the abstractive summarization task. Later, [PERSON] et al. [11] combined an attention mechanism with RNN, and utilized information such as stop words and document structure, and its ROUGE-L improved by 1.47% compared to [PERSON] on DUC-2004 and gigaword datasets. In 2018, [PERSON] et al. [12] used a content selector as a bottom-up attention step to constrain the model to likely phrases. This approach improves the ability to compress text, while still generating fluent summaries. [PERSON] et al. [13] introduced deep communication agents in an encoder-decoder architecture; these are divided into cooperative agents, each of which is responsible for representing a part of the text, to solve the challenges of representing a long document for abstractive summarization. Empirical results show that multiple communication encoders produce higher quality summaries compared to baseline methods. In addition, the generation of OOV words and redundant words also has a significant impact on summary generation. To this end, [PERSON] et al. [14] divided OOV words into two strategies, one is to find similar words in the preset vocabulary, the other is to use original text words instead, and the decoding method is judged by a two-layer perceptron during generation. [PERSON] et al. [15] proposed COPYNET, which added the probabilities of the output words of two modules, Generate-Mode and Copy-Mode, at the decoder to obtain the distribution of the final words. [PERSON] et al. [16] presented a pointer network that uses the weights of the input sequence as a pointer, and outputs the word probability distribution for the input sequence. Furthermore, [PERSON] et al. [6] proposed a method combining generation and replication to solve the problem of OOV words, and introduced a coverage mechanism to solve redundant words. In order to improve the performance of the abstract, researchers have analyzed the relationship between the original text and the abstract from different perspectives. [PERSON] et al. [17] proposed a novel approach to formulate, extract, encode, and inject hierarchical structure information explicitly into an extractive summarization model. The HiStruct model outperforms baseline collectively on CNN/Daily Mail, PubMed, and arXiv. [PERSON] et al. [18] present a dynamic latent extraction approach for abstractive summarization. The model treats the extracted text segments as latent variables and employs dynamic segment-level attention weights during decoding. Experimental results show that DYLE outperforms all existing methods on GovReport and QMSum. In recent years, researchers have considered the summary generation process as machine translation, and have proposed many models. However, there are still significant differences between summary generation and machine translation. It is sufficient for the summary to retain only the key information of the original text, rather than all of it. Thus, it would be better if the summary had good coverage of the topics of the original text. It is inadequately represented in current evaluation metrics since the widely used ROUGE metric is a recall-based approach.