Using only cross-document relationships for both generic and topic-focused multi-document summarizations

• Published in: Information retrieval (Boston) Vol. 11; no. 1; pp. 25 - 49
• Main Author: Wan, Xiaojun
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2008; Springer; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Content analysis; Data Mining and Knowledge Discovery; Data Structures and Information Theory; Documents; Exact sciences and technology; Experiments; Indexing. Classification. Abstracting; Indexing. Classification. Abstracting. Syntheses; Information and communication sciences; Information and document structure and analysis; Information processing and retrieval; Information retrieval; Information science. Documentation; Information Storage and Retrieval; Methods; Natural Language Processing (NLP); Pattern Recognition; Ranking; Sciences and techniques of general use; Studies; Topic-focused summarization; Multi-document summarization; Cross-document relationship; Graph-ranking; Rank statistic; Language processing; Probabilistic approach; Abstracting; Natural language; Graph method
• ISSN: 1386-4564; 1573-7659
• DOI: 10.1007/s10791-007-9037-5

In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the “votings” or “recommendations” between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002–DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.