Multi-document summarization via submodularity

  • Authors:

  • Jingxuan Li;Lei Li;Tao Li

  • Affiliations:

  • School of Computing and Information Sciences, Florida International University, Miami, USA 33199;School of Computing and Information Sciences, Florida International University, Miami, USA 33199;School of Computing and Information Sciences, Florida International University, Miami, USA 33199

  • Venue:
  • Applied Intelligence
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

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.