ABSTRACT
Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.
- Bollegala, D., Matsuo, Y., and Ishizuka, M. 2007. Measuring Semantic Similarity between Words using Web Search Engines. In Proceedings of 16th WWW, pp 757--766. Google ScholarDigital Library
- Brin, S. and Page, L. 1998. The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1-7): 107--117. Google ScholarDigital Library
- Cilibrasi, R. L. and Vitanyi, P. M. B. 2007. The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 3, pp 370--383. Google ScholarDigital Library
- DUC: http://www-nlpir.nist.gov/projects/duc/pubs.html.Google Scholar
- Erkan, G. and Radev, D. R. 2004. LexRank: Graph-based Centrality as Salience in Text Summarization, Journal of Artificial Intelligence Research 22:457--479. Google ScholarCross Ref
- Haveliwala, T. H. 2003. Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search. IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 4, pp 784--796. Google ScholarDigital Library
- Langville, A. N. and Meyer, C. D. 2004. Deeper Inside PageRank. Journal of Internet Mathematics, 1(3): 335--380.Google ScholarCross Ref
- Lin, C. Y. and Hovy, E. 2000. The Automated Acquisition of Topic Signature for Text Summarization. In Proceedings of 18th COLING, pp 495--501. Google ScholarDigital Library
- Lin, C. Y. and Hovy, E. 2003. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics, in Proceedings of HLT-NAACL, pp71--78. Google ScholarDigital Library
- Mani, I. and Maybury, M. T.(Eds.). 1999. Advances in Automatic Summarization. The MIT Press. Google ScholarDigital Library
- Mihalcea, R. 2004. Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization. In Proceedings of ACL 2004, Article No. 20. Google ScholarDigital Library
- Mihalcea, R. 2005. Language Independent Extractive Summarization. In Proceedings of ACL 2005, pp 49--52. Google ScholarDigital Library
- Jones, K. S. 2007. Automatic Summarising: The State of the art. Information Processing and Management 43: 1449--1481. Google ScholarDigital Library
- Kleinberg, J. M. 1999. Authoritative Sources in a Hyperlinked Environment. In Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms. Pp 668--677. Google ScholarDigital Library
- Otterbacher, J., Erkan, G., and Radev, D. R. 2005. Using Random Walks for Question-focused Sentence Retrieval. In Proceedings of HLT/EMNLP, pp 915--922. Google ScholarDigital Library
- Ouyang, Y., Li, S. J., and Li, W. J. 2007. Developing Learning Strategies for Topic-Based Summarization. In Proceedings of the 16th CIKM, pp 79--86. Google ScholarDigital Library
- Over, P., Dang, H., and Harman, D. 2007. DUC in Context, Information Processing and Management, 43(6): 1506--1520. Google ScholarDigital Library
- Radev, D. R., Jing, H. Y., Stys, M., and Tam, D. 2004. Centroid-based Summarization of Multiple Documents. Information Processing and Management, 40: 919--938. Google ScholarDigital Library
- Sahami, M.and Heliman, T. D. 2006. A Web-based Kernel Function for Measuring the Similarity of Short Text Snippets. In Proceedings of 15th WWW, pp 377--386. Google ScholarDigital Library
- Tombros, A. and Rijsbergen, C. J. v. 2004. Query-Sensitive Similarity Measures for Information Retrieval. Knowledge and Information Systems (2004) 6: 617--642. Google ScholarDigital Library
- Wan, X. Y., Yang, J. W., and Xiao, J. G. 2007. Towards Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction. In Proceedings of ACL.Google Scholar
- Zha, H. Y. 2002. Generic Summarization and Key Phrase Extraction using Mutual Reinforcement Principle and Sentence Clustering. In Proceedings of the 25th ACM SIGIR, pp113--120. Google ScholarDigital Library
- Zhou, Z. H. and Dai, H. B. 2006. Query-Sensitive Similarity Measure for Content-Based Image Retrieval. In Proceedings of ICDM, pp1211--1215. Google ScholarDigital Library
Index Terms
- Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization
Recommendations
A Query-Sensitive Graph-Based Sentence Ranking Algorithm for Query-Oriented Multi-document Summarization
ISIP '08: Proceedings of the 2008 International Symposiums on Information ProcessingGraph-based models and ranking algorithms have been drawn considerable attentions from the document summarization community in the recent years. However, in regard to query-oriented summarization, the influence of the query has been limited to the ...
Query-Oriented Summarization Based on Neighborhood Graph Model
ICCPOL '09: Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based EconomyIn this paper, we investigate how to combine the <em>link-aware</em> and <em>link-free</em> information in sentence ranking for query-oriented summarization. Although the link structure has been emphasized in the existing graph-based summarization ...
Query-oriented unsupervised multi-document summarization via deep learning model
Highlights- First attempt of deep learning for query-oriented multi-document summarization.
AbstractCapturing the compositional process from words to documents is a key challenge in natural language processing and information retrieval. Extractive style query-oriented multi-document summarization generates a summary by extracting a ...
Comments