Multi-Document Summarization Use Semantic Similarity and Information Quantity of Sentence

Yeon-Soo Lim, Sunggoo Kwon, Bong-Min Kim, Seong-Bae Park

http://doi.org/10.5626/JOK.2023.50.7.561

Document summarization task has recently emerged as an important task in natural language processing because of the need for delivering concise information. However, it is difficult to obtain a suitable multi-document summarization dataset. In this paper, rather than training with a multi-document summarization dataset, we propose to use a single-document summarization dataset. That is, we propose a multi-document summarization model which generates multiple single-document summaries with a single-document summarization model and then post-processes these summaries. The proposed model consists of three modules: a summary module, a similarity module, and an information module. When multiple documents are entered into the proposed model, the summary module generates summaries of every single document. The similarity module clusters similar summaries by measuring semantic similarity. The information module selects the most informative summary from each similar summary group and collects selected summaries for the final multi-document summary. Experimental results show that the proposed model outperforms the baseline models and it can generate a high-quality multi-document summary. In addition, the performances of each module also show meaningful results.