篇名 |
TA-Sum: The Extractive Summarization Research Based on Topic Information
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並列篇名 | TA-Sum: The Extractive Summarization Research Based on Topic Information |
作者 | Fang Xie、Hao Li、Beiye Zhang、Jianan He、Xincong Zhong |
英文摘要 | Text summarization is divided into extractive summarization and abstractive summarization. The extractive summarization technology aims to extract some main phrases and sentences from the original text to form a short summary for people to read quickly. However, extractive summarization is faced with problems such as poor sentence coherence and incomplete information, which makes it difficult to screen out important sentences from the source text. DNN (Deep Neural Network) is widely used for text summarization task. This paper proposes a TA-Sum model based on the neural topic model. Introducing the topic information can help people understand the relevant main content of source text quickly. We obtain the topic information using the neural topic model and implement the attention mechanism to fuse the topic information with the text representation, which improves the semantic integrity and completeness of the summary. The experimental results on the large-scale English data sets CNN/Daily mail are improved by 0.37%, 0.11%, and 0.17% respectively compared with BertSum, which demonstrates the effectiveness of our method.
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起訖頁 | 1489-1496 |
關鍵詞 | Extractive summarization、Topic discovery、Attention mechanism、Neural network |
刊名 | 網際網路技術學刊 |
期數 | 202312 (24:7期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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