Abstract
In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document-document and document-sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks.
| Original language | English |
|---|---|
| Pages (from-to) | 245-259 |
| Number of pages | 15 |
| Journal | Knowledge and Information Systems |
| Volume | 22 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Feb 2010 |
Keywords
- Generic summarization
- Graph-based ranking algorithm
- Graph-based summarization model
- Inter- and intra-document relation
- Query-oriented summarization
ASJC Scopus subject areas
- Software
- Information Systems
- Human-Computer Interaction
- Hardware and Architecture
- Artificial Intelligence