Yan Li1, Xiaoguang Zhang1,*, Tianyu Gong1, Qi Dong1, Hailong Zhu1, Tianqiang Zhang1, Yanji Jiang2,3
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3691-3705, 2023, DOI:10.32604/cmc.2023.040492
- 08 October 2023
Abstract Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been More >
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