Research on Information Extraction of LCSTS Dataset Based on an Improved BERTSum-LSTM Model

TL;DR

This paper enhances the BERTSum-LSTM model to improve Chinese news summary generation, addressing language complexities and information extraction challenges in the LCSTS dataset.

Contribution

The paper introduces an improved BERTSum-LSTM model tailored for Chinese news summarization, demonstrating better performance on the LCSTS dataset.

Findings

Enhanced model achieves better summary quality.

Improved extraction of key information.

Significant impact on Chinese news summarization

Abstract

With the continuous advancement of artificial intelligence, natural language processing technology has become widely utilized in various fields. At the same time, there are many challenges in creating Chinese news summaries. First of all, the semantics of Chinese news is complex, and the amount of information is enormous. Extracting critical information from Chinese news presents a significant challenge. Second, the news summary should be concise and clear, focusing on the main content and avoiding redundancy. In addition, the particularity of the Chinese language, such as polysemy, word segmentation, etc., makes it challenging to generate Chinese news summaries. Based on the above, this paper studies the information extraction method of the LCSTS dataset based on an improved BERTSum-LSTM model. We improve the BERTSum-LSTM model to make it perform better in generating Chinese news summaries. The experimental results show that the proposed method has a good effect on creating news summaries, which is of great importance to the construction of news summaries.