Text summarization: BART, RF, and hybrid BART-RF algorithm comparison

Abstrak

Data and information accumulate quantitatively and qualitatively. Abundant text data are posted on the internet. The number correlates to the complexity of the summarization. Automatic text summarization (ATS) is one of the most challenging tasks in natural language processing (NLP). ATS approached in three ways: extractive, abstractive, and hybrid. Hybrid approach combines both extractive and abstractive. This research tests and compares performance of bidirectional auto-regressive transformer (BART) and random forest (RF) individually and the performance combination of hybrid BART and RF in ATS. The research shows that individually, BART and RF recall-oriented understudy for gisting evaluation (ROUGE) scores are having quite differences. Consecutively, ROUGE RF scores in R1, R2, and RL are 51.45, 45.52, and 54.58 respectively. Meanwhile, ROUGE BART scores are 32.78, 16.17, and 32.19. Consecutively, average ROUGE RF, BART, and RF×BART F-measure are 45.73, 21.38, and 31.31. RF has the highest average score. ATS hybrid RF×BART is shown to be performed better than the default BART. The average ROUGE F-measures for RF×BART obtain moderate score at 31.31. This score is better than the default BART’s ROUGE score. RF×BART can be an alternative to the effective hybrid approach.