Open Access

ARTICLE

Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

Ren Tao, Chen Shuang^*

Software College, Northeastern University, Shenyang, 110000, China

* Corresponding Author: Chen Shuang. Email:

Computers, Materials & Continua 2023, 75(3), 6201-6217. https://doi.org/10.32604/cmc.2023.036178

Received 20 September 2022; Accepted 29 December 2022; Issue published 29 April 2023

Abstract

A large variety of complaint reports reflect subjective information expressed by citizens. A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary. Therefore, in this paper, a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words. Furthermore, it considers the importance of entity in complaint reports to ensure factual consistency of summary. Experimental results on the customer review datasets (Yelp and Amazon) and complaint report dataset (complaint reports of Shenyang in China) show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation. It unveils the effectiveness of our approach to helping in dealing with complaint reports.

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Keywords

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