Extractive Summarization via Weighted Dissimilarity and Importance Aligned Key Iterative Algorithm
Table 2: Comparison of ROUGE scores between IMAKITA and existing algorithms.
['Algorithm', 'R-1', 'R-2']
ROUGE evaluates the similarity between sentences based on an n-gram co-occurrence. The accuracy of the summarization can be estimated by ROUGE score between generated summaries and human summaries. Opinosis dataset provides user reviews on 51 different topics and summarization of the review by 5 different human workers. Original Opinosis paper also provides ROUGE scores between different human summaries and it is an important criterion for the accuracy of the summarization. and IMAKITA. The number of the summary sentences is set to 2. One can see IMAKITA is as accurate as humans and existing methods. We also evaluated ROUGE score of IMAKITA without stopwords and the maximization of the weighted dissimilarity, respectively. Surprisingly, IMAKITA without stopwords has F1-score as high as SRSIM algorithm despite having no domain knowledge in English except words are separated by space and sentences are separated by period, probably because the effect of the meaningless words are canceled out inside the iterative greedy search because most of sentences have these stopwords. The result implies IMAKITA is easy to apply to other languages.