# An Abstract Summarization Method Combining Global Topics
###### Abstract  
Existing abstractive summarization methods only focus on the correlation between the original words and the summary words, ignoring the topics' influence on the summaries. To this end, an abstract summarization method combining global topic information, ACGT, is proposed. A topic information extractor, based on Latent Dirichlet Allocation, is constructed to extract key topic information from the original text, and an attention module is built to fuse key topic information with the original text representation. The summary is then generated by combining a pointer generation network and coverage mechanism. With evaluation metrics of ROUGE-1, ROUGE-2, and ROUGE-L, the experimental results of ACGT in the English dataset CNN/Daily Mail are 0.96%, 2.44%, and 1.03% higher than the baseline model, respectively. In the Chinese dataset, LCSTS, ACGT shows a higher performance than the baseline method by 1.19%, 1.03%, and 0.85%, respectively. Our results demonstrate that the performance of summaries is significantly correlated with the number of topics that are introduced. Case studies show that the introduction of topic information can improve both the coverage of original text topics and the fluency of summaries.  
natural language processing; automatic text summarization; topic model; pointer generation network; BERT +
Footnote †: journal: Medical  
words from a fixed vocabulary, and uses a coverage mechanism to alleviate the word redundancy problem, thus improving the performance of summary generation.  
In general, the Seq2 Seq framework combined with an attention mechanism provides the basis for studies of automatic summaries. However, existing approaches consider summary generation as a translation process from the original text to the summary; moreover, most of the attention mechanism is built between characters of the original text and the summary characters, with less research from the perspective of the topics of the original text. In terms of evaluation metrics of automated summaries, ROUGE [7], which is widely used, is a recall-based evaluation metric. It evaluates the n-tuple contribution statistics of the generated summary and the reference summary, without evaluating the topic consistency between the summary and original text, which may result in topics of the summaries deviating from the original text despite high ROUGE scores.  
We argue that the process of manually writing summaries is to read and understand the full text to find the words and phrases that are most relevant to the original topic, and then write a summary that matches the original topic. Therefore, we propose an Abstract Summarization method Combining Global Topics (ACGT) to improve the performance of the summary by constructing a global topic information extractor and an attention module that combines topic information. Our main achievements in this paper are as follows:  
1. We propose a summary generation method incorporating global topic information, model the topic of the document, and update the document representation by fusing the topic information of the text with word embeddings through the information fusion module.
2. We propose the nTmG (n-TOPIC-m-GRAM) method to extract the key topic information in the original text. The essential method is used to avoid the noise caused by the introduction of the topic.
3. Empirical studies show that the proposed method demonstrates a more advanced performance than baseline methods. It also shows that the number of incorporated topics is tightly correlated with the performance of generating summaries, which provides empirical evidence for subsequent automatic summary studies combining topics.