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Multi-document summarization via submodularity

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Abstract

Multi-document summarization is becoming an important issue in the Information Retrieval community. It aims to distill the most important information from a set of documents to generate a compressed summary. Given a set of documents as input, most of existing multi-document summarization approaches utilize different sentence selection techniques to extract a set of sentences from the document set as the summary. The submodularity hidden in the term coverage and the textual-unit similarity motivates us to incorporate this property into our solution to multi-document summarization tasks. In this paper, we propose a new principled and versatile framework for different multi-document summarization tasks using submodular functions (Nemhauser et al. in Math. Prog. 14(1):265–294, 1978) based on the term coverage and the textual-unit similarity which can be efficiently optimized through the improved greedy algorithm. We show that four known summarization tasks, including generic, query-focused, update, and comparative summarization, can be modeled as different variations derived from the proposed framework. Experiments on benchmark summarization data sets (e.g., DUC04-06, TAC08, TDT2 corpora) are conducted to demonstrate the efficacy and effectiveness of our proposed framework for the general multi-document summarization tasks.

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  1. School of Computing and Information Sciences, Florida International University, 11200 SW 8th St, Miami, FL, 33199, USA

    Jingxuan Li, Lei Li & Tao Li

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  1. Jingxuan Li
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  2. Lei Li
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  3. Tao Li
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Correspondence to Tao Li.

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Li, J., Li, L. & Li, T. Multi-document summarization via submodularity. Appl Intell 37, 420–430 (2012). https://doi.org/10.1007/s10489-012-0336-1

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