Scientific document summarization in multi-objective clustering framework

The exponential growth in the number of scientific articles has made it difficult for the researchers to keep themselves updated with the new developments. Scientific document summarization solves this problem by providing a summary of essential contributions. In this paper, we have presented a novel method of scientific document summarization using a multi-objective differential evolution technique. Here, firstly distinct important sentences are extracted by using citation contextualization. These sentences are further clustered using the concept of multi-objective clustering. Two objective functions, PBM index, and XB index, measuring the compactness and separation of sentence clusters, are simultaneously optimized utilizing the search capability of multi-objective differential evolution. We have conducted our experiments on CL-SciSumm 2016, CL-SciSumm 2017, CL-SciSumm 2018, and CL-SciSumm 2019 datasets. Obtained results of CL-SciSumm 2016 and CL-SciSumm 2017 are compared with the state-of-the-art methods. Evaluation results demonstrate that our method outperforms others in terms of ROUGE-2, ROUGE-3, and ROUGE-SU4 scores.