Improving Document Summarization by Incorporating Social Contextual Information

We propose a collaborative approach to improve document summarization by incorporating social contextual information into the sentence ranking process. Both the relationships between sentences from document context and the preference information from user context are investigated in the approach. We validate our method on a social tagging dataset and experimentally demonstrate that by incorporating social contextual information it obtains significant improvement over several baseline methods.

[1]  Hongyuan Zha,et al.  Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering , 2002, SIGIR '02.

[2]  Xiaojun Wan,et al.  Manifold-Ranking Based Topic-Focused Multi-Document Summarization , 2007, IJCAI.

[3]  Qin Lu,et al.  Applying regression models to query-focused multi-document summarization , 2011, Inf. Process. Manag..

[4]  Xin Liu,et al.  Generic text summarization using relevance measure and latent semantic analysis , 2001, SIGIR '01.

[5]  Dianne P. O'Leary,et al.  Text summarization via hidden Markov models , 2001, SIGIR '01.

[6]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[7]  Xiaojun Wan Using only cross-document relationships for both generic and topic-focused multi-document summarizations , 2007, Information Retrieval.

[8]  Xiaojun Wan,et al.  CollabSum: exploiting multiple document clustering for collaborative single document summarizations , 2007, SIGIR.

[9]  Jade Goldstein-Stewart,et al.  The use of MMR, diversity-based reranking for reordering documents and producing summaries , 1998, SIGIR '98.

[10]  Sun Park,et al.  Automatic generic document summarization based on non-negative matrix factorization , 2009, Inf. Process. Manag..

[11]  Yuji Matsumoto,et al.  A new approach to unsupervised text summarization , 2001, SIGIR '01.

[12]  Dragomir R. Radev,et al.  LexPageRank: Prestige in Multi-Document Text Summarization , 2004, EMNLP.

[13]  Michael Gamon,et al.  The PYTHY Summarization System: Microsoft Research at DUC 2007 , 2007 .

[14]  Weiguo Fan,et al.  Automatic summarization of search engine hit lists , 2000 .

[15]  Bernadette Bouchon-Meunier,et al.  Web Document Summarization by Context , 2003, WWW.

[16]  Hua Li,et al.  Document Summarization Using Conditional Random Fields , 2007, IJCAI.

[17]  Xiaojun Wan,et al.  Single Document Summarization with Document Expansion , 2007, AAAI.

[18]  Dragomir R. Radev,et al.  Introduction to the Special Issue on Summarization , 2002, CL.

[19]  Eduard H. Hovy,et al.  Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics , 2003, NAACL.

[20]  Marti A. Hearst,et al.  HLT-NAACL 2003 : Human Language Technology conference of the North American Chapter of the Association for Computational Linguistics : proceedings of the main conference : May 27 to June 1, 2003, Edmonton, Alberta, Canada , 2003 .

[21]  Alessandro Giuliani,et al.  Studying the Impact of Text Summarization on Contextual Advertising , 2011, 2011 22nd International Workshop on Database and Expert Systems Applications.