Hiroya Takamura
ABSTRACT
We propose a multi-document generic summarization model based on the budgeted median problem. Our model selects sentences to generate a summary so that every sentence in the document cluster can be assigned to and be represented by a sentence in the summary as much as possible. The advantage of this model is that it covers the entire relevant part of the document cluster through sentence assignment and can incorporate asymmetric relations between sentences such as textual entailment.
- R. Bar-Haim, I. Dagan, B. Dolan, L. Ferro, D. Giampiccolo, B. Magnini, and I. Szpektor. The second pascal recognising textual entailment challenge. In Proc. of the 2nd PASCAL Challenges Workshop on Recognising Textual Entailment, pp. 1--9, 2006.Google Scholar
- J. M. Conroy, J. D. Schlesinger, J. Goldstein, and D. P. O'Leary. Left-brain/right-brain multi-document summarization. In Proc. of the DUC, 2004.Google Scholar
- Z. Drezner and H. W. Hamacher, editors. Facility Location: Applications and Theory. Springer, 2004.Google Scholar
- Document Understanding Conference. HLT/NAACL Workshop on Text Summarization, 2004.Google Scholar
- E. Filatova and V. Hatzivassiloglou. A formal model for information selection in multi-sentence text extraction. In Proc. of the 20th COLING, pp. 397--403, 2004. Google ScholarDigital Library
- J. Hromkovič. Algorithmics for Hard Problems. Springer, 2003. Google ScholarDigital Library
- C. Lin. ROUGE: a package for automatic evaluation of summaries. In Proc. of the Workshop on Text Summarization Branches Out, pp. 74--81, 2004.Google Scholar
- I. Mani. Automatic Summarization. John Benjamins Publisher, 2001.Google Scholar
- R. McDonald. A study of global inference algorithms in multi-document summarization. In Proc. of the 29th ECIR, pp. 557--564, 2007. Google ScholarDigital Library
- T. Nomoto and Y. Matsumoto. A new approach to unsupervised text summarization. In Proc. of the 24th SIGIR, pp. 26--34, 2001. Google ScholarDigital Library
- D. R. Radev, H. Jing, M. gorzata Styś, and D. Tam. Centroid-based summarization of multiple documents. Information Processing and Management, 40(6):919--938, 2004. Google ScholarDigital Library
- P. Rojeski and C. S. ReVelle. Central facilities location under an investment constraint. Geographical Analysis, 2:343--360, 1970.Google ScholarCross Ref
- V. Rus, A. Graesser, P. M. McCarthy, and K.-I. Lin. A study on textual entailment. In Proc. of the 17th ICTAI, pp. 326--333, 2005. Google ScholarDigital Library
- D. B. Shmoys. Approximation algorithms for facility location problems. In Approximation Algorithms for Combinatorial Optimization (LNCS; Vol. 1913), pp. 369--378, 2000. Google ScholarDigital Library
- H. Takamura and M. Okumura. Text summarization model based on maximum coverage problem and its variant. In Proc. of the 12th EACL, pp. 781--789, 2009. Google ScholarDigital Library
- D. Zelenko, C. Aone, and A. Richardella. Kernel methods for relation extraction. Journal of Machine Learning Research, 3:1083--1106, 2003. Google ScholarDigital Library
Index Terms
- Text summarization model based on the budgeted median problem
Recommendations
Multi-document Hyperedge-based Ranking for Text Summarization
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementIn a multi-document settings, graph-based extractive summarization approaches build a similarity graph out of sentences in each cluster of documents then use graph centrality approaches to measure the importance of sentences. The similarity is computed ...
Graph-based abstractive biomedical text summarization
Graphical abstractDisplay Omitted
Highlights- A graph generation and frequent itemset mining approach have been used for the generation of extractive summaries.
AbstractSummarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. ...
A Model for Text Summarization
Text summarization is a process for creating a concise version of documents preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At ...
Comments