The great challenge in Indonesian automatic text summarization research is producing readable summaries. The quality of text summary can be reached if the meaning of the text can be maintained properly. As a result, the purpose of this study is to improve the quality of extractive Indonesian automatic text summarization by taking into account the quality of structured text representation. This study employs Sequential Pattern Mining (SPM) to generate a sequence of words as a structured representation of text and a graph-based approach to generate automatic text summarization. The SPM algorithm used is PrefixSpan, and the graph-based approach uses the Bellman-Ford algorithm. The results of an experiment using the IndoSum dataset show that combining SPM and Bellman-Ford can improve the precision, recall, and f-measure of ROUGE-1, ROUGE-2, and ROUGE-L. When Bellman-Ford is combined with SPM, the F-measure of ROUGE-1 increases from 0.2299 to 0.3342. The ROUGE-2 f-measure increases from 0.1342 to 0.2191, and the ROUGE-L f-measure increases from 0.1904 to 0.2878. This result demonstrates that SPM can improve the performance of the Bellman-Ford algorithm in producing Indonesian text summaries.
Maylawati et al. (Sun,) studied this question.