  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.
