Abstract
Graph ranking algorithms have been successfully used in multi-document summarization. Among them, the basic link analysis model has drawn much attention due to its' mutual reinforcement principle which appears to be sound for the generic summarization task. In this paper, we explore effective strategies for extending the basic link analysis model to question-oriented multi-document summarization. Three kinds of strategies, namely link re-weighting, baseset downsizing and projection, are proposed to introduce question-dependent similarity metric, adjust the node number and refine the ranking process respectively. Experimental results evaluated on the DUC data sets demonstrate that these three strategies can achieve better results.
| Original language | English |
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| Title of host publication | Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 |
| Pages | 1896-1901 |
| Number of pages | 6 |
| Volume | 4 |
| DOIs | |
| Publication status | Published - 7 Nov 2011 |
| Event | 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011 |
Conference
| Conference | 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 |
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| Country/Territory | China |
| City | Guilin, Guangxi |
| Period | 10/07/11 → 13/07/11 |
Keywords
- Baseset Downsizing
- Link Analysis Model
- Link Re-weighting
- Projection
- Question-oriented Multi-document Summarization
ASJC Scopus subject areas
- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Networks and Communications
- Human-Computer Interaction