Diverse Keyword Extraction from Conversations

A new method for keyword extraction from conversations is introduced, which preserves the diversity of topics that are mentioned. Inspired from summarization, the method maximizes the coverage of topics that are recognized automatically in transcripts of conversation fragments. The method is evaluated on excerpts of the Fisher and AMI corpora, using a crowdsourcing platform to elicit comparative relevance judgments. The results demonstrate that the method outperforms two competitive baselines.

[1]  Rada Mihalcea,et al.  Linking Educational Materials to Encyclopedic Knowledge , 2007, AIED.

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

[3]  David Miller,et al.  The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text , 2004, LREC.

[4]  Feifan Liu,et al.  Unsupervised Approaches for Automatic Keyword Extraction Using Meeting Transcripts , 2009, NAACL.

[5]  Tat-Seng Chua,et al.  Document concept lattice for text understanding and summarization , 2007, Inf. Process. Manag..

[6]  SaltonGerard,et al.  Term-weighting approaches in automatic text retrieval , 1988 .

[7]  Yi-fang Brook Wu,et al.  Domain-specific keyphrase extraction , 2005, CIKM '05.

[8]  Timothy J. Hazen,et al.  Topic identification based extrinsic evaluation of summarization techniques applied to conversational speech , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Zhiyuan Liu,et al.  Clustering to Find Exemplar Terms for Keyphrase Extraction , 2009, EMNLP.

[10]  Hans Peter Luhn,et al.  A Statistical Approach to Mechanized Encoding and Searching of Literary Information , 1957, IBM J. Res. Dev..

[11]  Clement T. Yu,et al.  A theory of term importance in automatic text analysis , 1974, J. Am. Soc. Inf. Sci..

[12]  Hui Lin,et al.  A Class of Submodular Functions for Document Summarization , 2011, ACL.

[13]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[14]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[15]  Maryam Habibi,et al.  Using Crowdsourcing to Compare Document Recommendation Strategies for Conversations , 2012, RUE@RecSys.

[16]  Peter D. Turney.  Learning to Extract Keyphrases from Text , 2002, ArXiv.

[17]  Ani Nenkova,et al.  A Survey of Text Summarization Techniques , 2012, Mining Text Data.

[18]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[19]  Mitsuru Ishizuka,et al.  Keyword extraction from a single document using word co-occurrence statistical information , 2004, Int. J. Artif. Intell. Tools.

[20]  Jean Carletta,et al.  Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus , 2007, Lang. Resour. Evaluation.

[21]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[22]  Tao Li,et al.  Multi-document summarization via submodularity , 2012, Applied Intelligence.

[23]  Cong Wang,et al.  Keyword Extraction Based on PageRank , 2007, PAKDD.

[24]  Zhiyuan Liu,et al.  Automatic Keyphrase Extraction via Topic Decomposition , 2010, EMNLP.

[25]  Anette Hulth,et al.  Improved Automatic Keyword Extraction Given More Linguistic Knowledge , 2003, EMNLP.

[26]  Charles L. A. Clarke,et al.  Novelty and diversity in information retrieval evaluation , 2008, SIGIR '08.

[27]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..