An event-based opinion summarization model for long chinese text with sentiment awareness and parameter fusion mechanism

During the outbreak of a specific social event, end-to-end automatic opinion summarization is needed to analyze the surge of text related to the event. However, in the Chinese domain, the major existing works either emphasize salient aspects or sentence extraction in a discrete fashion with no consideration of human readability, or focus on short Chinese text. To remedy the drawbacks of these methods, in this paper, an event-based opinion summarization model for long Chinese text with a parameter fusion mechanism is proposed to address the human readability and imbalance issue of the event-based datasets. In particular, to capture the sentiment information in the source article in an end-to-end manner, a sentiment attention layer and a sentiment cross-entropy loss function are presented. In addition, when facing the issue of imbalance in event-based datasets, a parameter fusion mechanism inspired by the federated learning is proposed, which can further improve the human readability of the output. Finally, the efficacy of the proposed model is substantiated via comprehensive experiments performed on the collected event-based datasets, the Chinese long text summarization dataset (CLTS), and the cable news network/daily mail (CNN/DM) dataset using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and sentiment classification accuracy metrics. In addition, the source code is made available at https://github.com/ShawnYoung97/opinion-sum .