Weighted hierarchical archetypal analysis for multi-document summarization. (May 2016)

• Record Type: Journal Article
• Authors: Canhasi, Ercan; Kononenko, Igor
• Is Part Of: Computer speech & language. Volume 37(2016)
• Page Start: 24
• Page End: 46
• Publication Date: 2016-05
• Subjects: Multi-document summarization framework -- Weighted hierarchical archetypal analysis -- General -- Query-focused -- Update -- Comparative summarization; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2015.11.004
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: We formalize the problem of MDS as the weighted hierarchical archetypal analysis problem. The weighted hierarchical archetypal analysis shows the ability to select "the best of the best" summary sentences. The paper demonstrates how four different summarization tasks can be treated by the proposed framework. Experimental results show that the framework is an effective summarization method. Abstract: 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 "the best of the best" 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.