Desentiment: A New Method to Control Sentimental Tendency During Summary Generation

Abstractive summarization tasks are commonly without options for sentimental tendencies, which leads to a lack of summary personalization and a simplification of the understanding of the text content. Recognizing the crucial role of sentimental tendency in shaping reader interest and perception, such as prompting hopeful outlooks or critical evaluations, we introduce the summaries with multiple optional sentimental tendencies (SMOST) task, which involves generating summaries with various sentiment options and particularly benefits the news domain. Due to a scarcity of labeled data for sentiment-supervised summarization, we utilize sentiment sentences from original texts as positive samples in the training process, augmented with a prompt learning method. Our method achieves a better result on the CNN/DailyMail and XSum datasets regarding sentiment scores and has a small influence on the semantic information of summaries. Further analysis also shows that our method can present the different distributions of sentiment and semantic information on different datasets.