Efficient Voting-Based Extractive Automatic Text Summarization Using Prominent Feature Set

ABSTRACT Automatic text summarization (ATS) is the process of generating a summary by condensing text document by a computer machine. In this paper, we explored voting-based extractive approaches for text summarization. The main issue with most of the feature-based ATS methods is to find optimal feature weights for sentence scoring to optimize the quality of summary. Voting-based methods are sensitive to initial ranking process. We proposed reciprocal ranking-based sentence scoring approach that alleviates the feature weighting and initial ranking problem. The proposed approach uses a specific prominent set of features for initial ranking that further enhance the performance. Experimental results on Document Understating Conference 2002 data-set using ROUGE evaluation matrices shows that our proposed method performs better as compared to other voting-based methods.

[1]  Rajesh S. Prasad,et al.  Implementation and Evaluation of Evolutionary Connectionist Approaches to Automated Text Summarization , 2010 .

[2]  Yogesh Kumar Meena,et al.  Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization , 2014, ICTCS '14.

[3]  Edward A. Fox,et al.  Combination of Multiple Searches , 1993, TREC.

[4]  Breck Baldwin,et al.  Dynamic Coreference-Based Summarization , 1998, EMNLP.

[5]  Naomie Salim,et al.  Voting Models for Summary Extraction from Text Documents , 2014, 2014 International Conference on IT Convergence and Security (ICITCS).

[6]  Javed A. Aslam,et al.  Models for metasearch , 2001, SIGIR '01.

[7]  Antonio Zamora,et al.  Automatic Abstracting Research at Chemical Abstracts Service , 1975, J. Chem. Inf. Comput. Sci..

[8]  Naomie Salim,et al.  Differential evolution cluster-based text summarization methods , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

[9]  Eduard H. Hovy,et al.  Automated Text Summarization and the SUMMARIST System , 1998, TIPSTER.

[10]  Lisa F. Rau,et al.  Automatic Condensation of Electronic Publications by Sentence Selection , 1995, Inf. Process. Manag..

[11]  Ralph Grishman,et al.  Summarization System Integrated with Named Entity Tagging and IE pattern Discovery , 2002, LREC.

[12]  Xiaoyue Liu,et al.  An Extractive Text Summarizer Based on Significant Words , 2009, ICCPOL.

[13]  Mark T. Maybury,et al.  Advances in Automatic Text Summarization , 1999 .

[14]  Chin-Yew Lin,et al.  ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.

[15]  Dragomir R. Radev,et al.  Experiments in Single and Multi-Document Summarization Using MEAD , 2001 .

[16]  George D. C. Cavalcanti,et al.  Assessing sentence scoring techniques for extractive text summarization , 2013, Expert Syst. Appl..

[17]  James E. Rush,et al.  Automatic abstracting and indexing. II. Production of indicative abstracts by application of contextual inference and syntactic coherence criteria , 1971 .

[18]  Jun Ma,et al.  A Comprehensive Method for Text Summarization Based on Latent Semantic Analysis , 2013, NLPCC.

[19]  Craig MacDonald,et al.  Voting for candidates: adapting data fusion techniques for an expert search task , 2006, CIKM '06.

[20]  Rafael Dueire Lins,et al.  A Context Based Text Summarization System , 2014, 2014 11th IAPR International Workshop on Document Analysis Systems.

[21]  Fuji Ren,et al.  GA, MR, FFNN, PNN and GMM based models for automatic text summarization , 2009, Comput. Speech Lang..

[22]  Vasudeva Varma,et al.  Sentence Extraction Based Single Document Summarization , .

[23]  Naomie Salim,et al.  Text summarization features selection method using pseudo Genetic-based model , 2012, 2012 International Conference on Information Retrieval & Knowledge Management.

[24]  Gerard Salton,et al.  Automatic Text Structuring and Summarization , 1997, Inf. Process. Manag..

[25]  Edward A. Fox,et al.  Research Contributions , 2014 .

[26]  A. Kogilavani,et al.  CLUSTERING AND FEATURE SPECIFIC SENTENCE EXTRACTION BASED SUMMARIZATION OF MULTIPLE DOCUMENTS , 2010 .

[27]  Masaki Murata,et al.  Sentence Extraction System Assembling Multiple Evidence , 2001, NTCIR.

[28]  Craig MacDonald,et al.  Voting techniques for expert search , 2008, Knowledge and Information Systems.

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

[30]  Kenneth Ward Church,et al.  Inverse Document Frequency (IDF): A Measure of Deviations from Poisson , 1995, VLC@ACL.

[31]  H. P. Edmundson,et al.  New Methods in Automatic Extracting , 1969, JACM.

[32]  Elizabeth León Guzman,et al.  Extractive single-document summarization based on genetic operators and guided local search , 2014, Expert Syst. Appl..

[33]  Eduard H. Hovy,et al.  Identifying Topics by Position , 1997, ANLP.

[34]  Karen Spärck Jones.  Automatic summarising: factors and directions , 1998, ArXiv.

[35]  Tatsunori Mori,et al.  Information Gain Ratio as Term Weight: The case of Summarization of IR Results , 2002, COLING.

[36]  Phyllis B. Baxendale,et al.  Machine-Made Index for Technical Literature - An Experiment , 1958, IBM J. Res. Dev..

[37]  James P. Callan,et al.  Combining document representations for known-item search , 2003, SIGIR.

[38]  Naomie Salim,et al.  Multi document summarization based on cross-document relation using voting technique , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

[39]  Hans Peter Luhn,et al.  The Automatic Creation of Literature Abstracts , 1958, IBM J. Res. Dev..

[40]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[41]  Wenjie Li,et al.  Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization , 2010, COLING.