A new multi-objective evolutionary algorithm for citation-based summarization: Comprehensive analysis of the generated summaries. (March 2023) - Sanchez-Gomez, Jesus M.; Vega-Rodríguez, Miguel A.; Pérez, Carlos J.
Abstract: The number of scientific publications in different knowledge fields has considerably grown in recent times. This makes difficult for researchers to synthesize all the scientific-technical advances, so automatic summarization methods of scientific papers would be helpful. These methods generate a summary from a reference paper with its most relevant contributions. More specifically, citation-based summarization considers the citation contexts to the reference paper in subsequent publications. For the first time, this problem has been formulated as a multi-objective optimization problem, optimizing the content coverage and the redundancy reduction in a simultaneous way. A Decomposition-based Multi-Objective optimization algorithm for Citation-based Summarization (DMOCS) has been designed, developed, and applied for solving this problem. The results obtained by the proposed approach have improved the existing ones in the scientific literature between 17.47% and 133.50%, increasing the ROUGE percentage improvements when the N -gram is larger. Besides, an exhaustive analysis of the different parts of a scientific paper has been performed, showing that the citations from the citing papers with their corresponding spans in the reference paper impact in the quality of a citation-based summary. Highlights: First time the citation-based summarization is solved by multi-objective optimization. A decomposition-based multi-objective optimization algorithm (DMOCS) is proposed. An extensive analysis of the best text parts for citation-based summarization is done. Interesting conclusions about the citing spans and the citation contexts are obtained. DMOCS improves the results of the different approaches in the scientific literature.