Open Access

ARTICLE

Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

by Yan Li¹, Xiaoguang Zhang^1,*, Tianyu Gong¹, Qi Dong¹, Hailong Zhu¹, Tianqiang Zhang¹, Yanji Jiang^2,3

1 Engineering Technical Department, China FAW Group Co., Ltd., Changchun, 130012, China
2 Suzhou Automotive Research Institute, Tsinghua University, Suzhou, 215200, China
3 College of Software, Liaoning Technical University, Fuxin, 125105, China

* Corresponding Author: Xiaoguang Zhang. Email:

Computers, Materials & Continua 2023, 76(3), 3691-3705. https://doi.org/10.32604/cmc.2023.040492

Received 20 March 2023; Accepted 21 July 2023; Issue published 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 developed. However, similar to traditional summarization models, the generated summaries still exist problems of informativeness and conciseness. To address these issues and generate suitably informative and less redundant summaries, a topic-based abstractive summarization method is proposed to obtain global and local features. Additionally, a heterogeneous graph of the original document is constructed using word-level and topic-level features. Experiments and analyses on public review datasets (Yelp and Amazon) and our constructed dataset (Chinese complaint reports) show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.

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Keywords

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