Multi-document summarization (MDS) is becoming a crucial task in natural language processing. MDS targets to condense the most important information from a set of documents to produce a brief summary. Most existing extractive multi-document summarization methods employ different sentence selection approaches to obtain the summary as a subset of sentences from the given document set. The ability of the weighted hierarchical archetypal analysis to select Ã¢ÂÂthe best of the bestÃ¢ÂÂ summary sentences motivates us to use this method in our solution to multi-document summarization tasks. In this paper, we propose a new framework for various multi-document summarization tasks based on weighted hierarchical archetypal analysis. The paper demonstrates how four variant summarization tasks, including general, query-focused, update, and comparative summarization, can be modeled as different versions acquired from the proposed framework. Experiments on summarization data sets (DUC04-07, TAC08) are conducted to demonstrate the efficiency and effectiveness of our framework for all four kinds of the multi-document summarization tasks. 
Weighted hierarchical archetypal analysis for multi-document summarization