Optimizing Data-Driven Models for Summarization as Parallel Tasks

This paper presents tackling of a hard optimization problem of computational linguistics, specifically automatic multi-document text summarization, using grid computing. The main challenge of multi-document summarization is to extract the most relevant and unique information effectively and efficiently from a set of topic-related documents, constrained to a specified length. In the Big Data/Text era, where the information increases exponentially, optimization becomes essential in selection of the most representative sentences for generating the best summaries. Therefore, a data-driven summarization model is proposed and optimized during a run of Differential Evolution (DE). Different DE runs are distributed to a grid in parallel as optimization tasks, seeking high processing throughput despite the demanding complexity of the linguistic model, especially on longer multi-documents where DE improves results given more iterations. Namely, parallelization and the grid enable, running several independent DE runs at same time within fixed real-time budget. Such approach results in improving a Document Understanding Conference (DUC) benchmark recall metric over a previous setting.