Parallelizing a multi-objective optimization approach for extractive multi-document text summarization

Currently, automatic multi-document text summarization is an important task in many fields of knowledge, due to the continuous exponential growth of information on the Internet. Nevertheless, this task is computationally demanding. In the last years, automatic text summarization has been addressed by using multi-objective optimization approaches. In particular, recently, the Multi-Objective Artificial Bee Colony (MOABC) algorithm has obtained very good results. This work focuses on the parallelization of this approach. Several steps have been carried out for this goal. After a time profiling of the algorithm, a runtime comparison has been performed between the use of different random number generators within the algorithm. Then, a parallel implementation of the MOABC algorithm has been designed following its original scheme, in which the main steps are parallelized, and different parallel schedules have been studied and compared. Finally, a second design based on the asynchronous behavior of the bee colony in nature has been implemented and compared. Experiments have been carried out with datasets from Document Understanding Conference (DUC). The results show that the asynchronous design improves greatly the parallel design, being more than 55 times faster with 64 threads than the standard design. An efficiency of 86.72% has been reported for 64 threads. (C) 2019 Elsevier Inc. All rights reserved.