CNsum:Automatic Summarization for Chinese News Text

TL;DR

This paper introduces CNsum, a Transformer-based model for automatic summarization of Chinese news texts, demonstrating improved performance over baseline models on Chinese datasets.

Contribution

The paper presents a novel Transformer-based model specifically designed for Chinese news text summarization, applying it to Chinese datasets and showing superior results.

Findings

CNsum outperforms baseline models in ROUGE scores

Transformer structure effectively applied to Chinese text summarization

Experimental results verify the model's superior performance

Abstract

Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model.