Multi-Document Summarization Method of Reviews Using Word Embedding Clustering


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 11, pp. 535-540, Nov. 2021
https://doi.org/10.3745/KTSDE.2021.10.11.535,   PDF Download:
Keywords: Muti-document, Text Summarization, Transformer, Word Embedding
Abstract

Multi-document refers to a document consisting of various topics, not a single topic, and a typical example is online reviews. There have been several attempts to summarize online reviews because of their vast amounts of information. However, collective summarization of reviews through existing summary models creates a problem of losing the various topics that make up the reviews. Therefore, in this paper, we present method to summarize the review with minimal loss of the topic. The proposed method classify reviews through processes such as preprocessing, importance evaluation, embedding substitution using BERT, and embedding clustering. Furthermore, the classified sentences generate the final summary using the trained Transformer summary model. The performance evaluation of the proposed model was compared by evaluating the existing summary model, seq2seq model, and the cosine similarity with the ROUGE score, and performed a high performance summary compared to the existing summary model.


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Cite this article
[IEEE Style]
P. W. Lee, Y. Y. Hwang, J. S. Choi, Y. T. Shin, "Multi-Document Summarization Method of Reviews Using Word Embedding Clustering," KIPS Transactions on Software and Data Engineering, vol. 10, no. 11, pp. 535-540, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.535.

[ACM Style]
Pil Won Lee, Yun Young Hwang, Jong Seok Choi, and Young Tae Shin. 2021. Multi-Document Summarization Method of Reviews Using Word Embedding Clustering. KIPS Transactions on Software and Data Engineering, 10, 11, (2021), 535-540. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.535.