Simin Liu

Yiming Chen, Haobin Chen, Simin Liu et al.

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.

Simin Liu

In recent years, deep learning has become more and more mature, and as a commonly used algorithm in deep learning, convolutional neural networks have been widely used in various visual tasks. In the past, research based on deep learning algorithms mainly relied on hardware such as GPUs and CPUs. However, with the increasing development of FPGAs, both field programmable logic gate arrays, it has become the main implementation hardware platform that combines various neural network deep learning algorithms This article is a review article, which mainly introduces the related theories and algorithms of convolution. It summarizes the application scenarios of several existing FPGA technologies based on convolutional neural networks, and mainly introduces the application of accelerators. At the same time, it summarizes some accelerators' under-utilization of logic resources or under-utilization of memory bandwidth, so that they can't get the best performance.