CollabSum: exploiting multiple document clustering for collaborative single document summarizations

ABSTRACT

Almost all existing methods conduct the summarization tasks for single documents separately without interactions for each document under the assumption that the documents are considered independent of each other. This paper proposes a novel framework called CollabSum for collaborative single document summarizations by making use of mutual influences of multiple documents within a cluster context. In this study, CollabSum is implemented by first employing the clustering algorithm to obtain appropriate document clusters and then exploiting the graph-ranking based algorithm for collaborative document summarizations within each cluster. Both the with-document and cross-document relationships between sentences are incorporated in the algorithm. Experiments on the DUC2001 and DUC2002 datasets demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the summarization performance relies positively on the quality of document cluster.

REFERENCES

• 1: Massih-Reza Amini , Patrick Gallinari, The use of unlabeled data to improve supervised learning for text summarization, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564397]
• 2: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 3: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, v.30 n.1-7, p.107-117, April 1, 1998
• 4: Jaime Carbonell , Jade Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.335-336, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291025]
• 5: John M. Conroy , Dianne P. O'leary, Text summarization via hidden Markov models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.406-407, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384042]
• 6: H. P. Edmundson, New Methods in Automatic Extracting, Journal of the ACM (JACM), v.16 n.2, p.264-285, April 1969 [doi>10.1145/321510.321519]
• 7: ErKan, Günes, Radev, D. R.: LexPageRank: Prestige in Multi-Document Text Summarization. In Proceedings of EMNLP2004.
• 8: Yihong Gong , Xin Liu, Generic text summarization using relevance measure and latent semantic analysis, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.19-25, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383955]
• 9: Hovy, E., Lin, C. Y.: Automated Text Summarization in SUMMARIST. In Proceeding of ACL'1997/EACL'1997 Worshop on Intelligent Scalable Text Summarization, 1997.
• 10: A. K. Jain , M. N. Murty , P. J. Flynn, Data clustering: a review, ACM Computing Surveys (CSUR), v.31 n.3, p.264-323, Sept. 1999 [doi>10.1145/331499.331504]
• 11: Hongyan Jing, Sentence reduction for automatic text summarization, Proceedings of the sixth conference on Applied natural language processing, p.310-315, April 29-May 04, 2000, Seattle, Washington [doi>10.3115/974147.974190]
• 12: Hongyan Jing , Kathleen R. McKeown, Cut and paste based text summarization, Proceedings of the first conference on North American chapter of the Association for Computational Linguistics, p.178-185, April 29-May 04, 2000, Seattle, Washington
• 13: Jon M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), v.46 n.5, p.604-632, Sept. 1999 [doi>10.1145/324133.324140]
• 14: Kevin Knight , Daniel Marcu, Summarization beyond sentence extraction: a probabilistic approach to sentence compression, Artificial Intelligence, v.139 n.1, p.91-107, July 2002 [doi>10.1016/S0004-3702(02)00222-9]
• 15: Julian Kupiec , Jan Pedersen , Francine Chen, A trainable document summarizer, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, p.68-73, July 09-13, 1995, Seattle, Washington, United States [doi>10.1145/215206.215333]
• 16: Chin-Yew Lin , Eduard Hovy, Automatic evaluation of summaries using N-gram co-occurrence statistics, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.71-78, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073445.1073465]
• 17: Chin-Yew Lin , Eduard Hovy, The automated acquisition of topic signatures for text summarization, Proceedings of the 18th conference on Computational linguistics, p.495-501, July 31-August 04, 2000, Saarbrücken, Germany [doi>10.3115/990820.990892]
• 18: Luhn, H. P.: The Automatic Creation of literature Abstracts. IBM Journal of Research and Development, 1969, 2(2).
• 19: Daniel McDonald , Hsinchun Chen, Using sentence-selection heuristics to rank text segments in TXTRACTOR, Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries, July 14-18, 2002, Portland, Oregon, USA [doi>10.1145/544220.544226]
• 20: Mihalcea, R., Tarau, P.: TextRank: Bringing Order into Texts. In Proceedings of EMNLP2004.
• 21: Mihalcea, R. and Tarau, P.: A language independent algorithm for single and multiple document summarization. In Proceedings of IJCNLP2005.
• 22: Tadashi Nomoto , Yuji Matsumoto, A new approach to unsupervised text summarization, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.26-34, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383956]
• 23: Porter, M. F. An algorithm for suffix stripping. Program, 14(3): 130--137, 1980.
• 24: Shen, D., Sun, J. -T., Li, H., Yang, Q., and Chen, Z. Document Summarization using Conditional Random Fields. In Proceedings of IJCAI 2007.
• 25: H. Gregory Silber , Kathleen F. McCoy, Efficient text summarization using lexical chains, Proceedings of the 5th international conference on Intelligent user interfaces, p.252-255, January 09-12, 2000, New Orleans, Louisiana, United States [doi>10.1145/325737.325861]
• 26: SteinBack, M., Karypis, G., and Kumar, V. A comparison of document clustering techniques. In KDD Workshop on Text Mining, 1999.
• 27: Hongyuan Zha, Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564398]
• 28: Zhang, B., Li, H., Liu, Y., Ji, L., Xi, W., Fan, W., Chen, Z., and Ma, W. -Y. Improving web search results using affinity graph. In Proceedings of SIGIR2005.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Experimentation, Performance

Keywords:
abstracting methods, collabSum, collaborative summarization, graph-ranking algorithm, single document summarization