Multi-Document Summarization via the Minimum Dominating Set

Multi-document summarization aims to distill the most important information from a set of documents to generate a compressed summary. Given a sentence graph generated from a set of documents where vertices represent sentences and edges indicate that the corresponding vertices are similar, the extracted summary can be described using the idea of graph domination. In this paper, we propose a new principled and versatile framework for multi-document summarization using the minimum dominating set. We show that four wellknown summarization tasks including generic, query-focused, update, and comparative summarization can be modeled as different variations derived from the proposed framework. Approximation algorithms for performing summarization are also proposed and empirical experiments are conducted to demonstrate the effectiveness of our proposed framework.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Chao Shen, Tao Li
• Keywords: COLING 2010, Comparative Summarization, Computational Linguistics, Multi-document Summarization, Wellknown Summarization Tasks