Comparison Of Maximal Marginal Relevance ( MMR) And Textrank Automatic Text Summarization Methods In Jurnal - Muhammad Alif Al Fattah; Rizal Rizal; Rini Meiyanti (Faculty of Engineering, Malikussaleh University, 2024)
The purpose of this study is to find out the application of the Maximal Marginal Relevance (MMR) and TextRank methods in automatic text summarization in journals in viewing the best model value in text automatically. This study can also implement an automatic text summarization application and find out the comparison between MMR and TextRank in the text summarization process in journals. Next research will evaluate the performance of the two models in producing relevant and informative text summaries. The problem of this research is how to overcome the problem of summarizing text with the basic concept of summary in providing the essence or overall content text in a journal. The main focus of this research is to display significant and relevant information in a more organized form and to display values for the efficiency of which model is the best after comparing the two models. The results of this research are a decision support system for determining the quality of poor rice using the fuzzy madm yager model with a value of (MMR) 0.4 and top N(3). The results of the comparison of the Maximal Marginal Relevance (MMR) similarity values are 0.510 and the score is 0.510, while the TextRank similarity is 0.510 and the score is 0.015. Based on testing of the two models, the best value was obtained from the rextrank model with a score of 0.015. Keywords: maximal marginal relevance (mmr), textrank, summary