A Cue-based Hub-Authority Approach for Multi-Document Text Summarization

A new approach under the hub-authority framework is proposed that combines the text content with some cues and explores the sub-topics in the multi-documents by bringing the features of these sub- topics into graph-based sentence ranking algorithms.

Abstract

Multi-document extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Although some research has introduced the graph-based ranking algorithms such as PageRank and HITS into the text summarization, we propose a new approach under the hub-authority framework in this paper. Our approach combines the text content with some cues such as "cue phrase", "sentence length" and "first sentence" and explores the sub-topics in the multi-documents by bringing the features of these sub-topics into graph-based sentence ranking algorithms. We provide an evaluation of our method on DUC 2004 data. The results show that our approach is an effective graph-ranking schema in multi-document generic text summarization.