Graph-Based Query-Focused Multi-document Summarization Using Improved Affinity Graph
Year: 2015
Type: conference-paper
Abstract: Manifold ranking is one of the most competitive approaches for query-focused multi-document summarization. Despite its success for this task, it usually constructs a sentence affinity graph first based on inter-sentence content similarity, and then perform manifold ranking on the graph to score each sentence with the assumption that all the sentences live on a single manifold. Actually, for a document set to be summarized, the distribution of the sentences might form different, but related manifolds. This paper aims to generalize the basic manifold-ranking based approach to the more generic setting by introducing a novel affinity graph to estimate the similarity between sentences, which leverages both the local geometric structures and the contents of sentences jointly. Preliminary experimental results on the DUC datasets demonstrate the good effectiveness of the proposed approach.
Authors: Po Hu, Jiacong He, Yong Zhang
Institution: Central China Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,392
OpenAlex ID: W2395219616