Scientific Document Summarization using Citation Context and Multi-objective Optimization

The rate of publishing scientific articles is increasing day by day which has created difficulty for the researchers to learn about the recent advancements in a faster way. Also, relying on the abstract of these published articles is not a good idea as they cover only broad ideas of the article. The summarization of scientific documents (SDS) addresses this challenge. In this paper, we propose a system for SDS having two components: identifying the relevant sentences in the article using citation context; generation of the summary by posing SDS as a binary optimization problem. For the purpose of optimization, a metaheuristic evolutionary algorithm is utilized. In order to improve the quality of summary, various aspects measuring the relevance of sentences are simultaneously optimized using the concept of multi-objective optimization. Inspired by the popularity of graph-based algorithms like LexRank which is popularly used in solving summarization problems of different real-life applications, its impact is studied in fusion with our optimization framework. An ablation study is also performed to identify the most contributing aspects for the summary generation. We investigated the performance of our proposed framework on two datasets related to the computational linguistic domain, CL-SciSumm 2016 and CL-SciSumm 2017, in terms of ROUGE measures. The results obtained illustrate that our framework effectively improves other existing methods. Further, results are validated using the statistical paired t-test.